The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index; none of the 3 sources its claims cite is checked for support yet.
Learning To Retrieve Prompts for In-Context Learning
Open accessAbstract
In-context learning is a recent paradigm in natural language understanding, where a large pretrained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters.However, performance has been shown to strongly depend on the selected training examples (termed prompts).In this work, we propose an efficient method for retrieving prompts for in-context learning using annotated data and an LM.Given an inputoutput pair, we estimate the probability of the output given the input and a candidate training example as the prompt, and label training examples as positive or negative based on this probability.We then train an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time.We evaluate our approach on three sequence-tosequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes Efficient Prompt Retrieval (EPR), which uses a language model to label which training examples are good in-context prompts and trains a dense retriever from that signal. EPR substantially outperforms prior prompt-retrieval baselines on BREAK, MTOP, and SMCALFLOW.
Key findings
- With GPT-NEO as both scoring and inference LM, EPR improves over the best baseline from 26.0 to 31.9 on BREAK, from 57.0 to 64.2 on MTOP, and from 51.4 to 54.3 on SMCALFLOW.
- In the one-shot BREAK setting, EPR reaches 23.0 LF-EM, outperforming the best baseline by 8.5 points and BM25-ORACLE by 5 points.
- When GPT-NEO is used as a proxy scoring LM for larger inference LMs, EPR outperforms BM25 and CBR by 2–8 points across all datasets and pretrained models.
- Prompt-copying analysis shows high abstract copying in MTOP and SMCALFLOW (>80%); on MTOP, 84.5% of examples were abstractly copied, and EPR achieved 71.6% EM on that copied subset versus 64.2% overall.
Methods
- EPR generates training data by retrieving candidate prompts with an unsupervised retriever (BM25 or SBERT; BM25 chosen), scoring each candidate with a scoring LM as Prob(y | candidate, x), and labeling top-k as positive and bottom-k as negative (k=5, L=50).
- It trains a dense retriever with DPR-style contrastive learning using BERT-base input and prompt encoders, then uses FAISS maximum inner-product search at inference to retrieve prompts within the inference LM’s context limit.
- Evaluation covers three structured sequence-to-sequence tasks—BREAK, MTOP, and SMCALFLOW—using GPT-NEO as scoring LM and GPT-NEO, GPT-J, GPT-3, or CODEX as inference LMs, with LF-EM/NEM/EM metrics and a 2,048-token context size.
Limitations
- Paper-stated: the authors note that large language models exhibit bias, and since EPR is trained on signal from such LMs, it might also exhibit these biases.
- Paper-stated: the method scores each training example independently at training time, while at test time the LM observes a sequence of examples; modeling dependence between examples is left to future work.
- Evident from the text: evaluation is limited to three English structured tasks mapping utterances to meaning representations, and the approach relies on a scoring LM as a proxy for the inference LM.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
19 Own result · 11 Own interpretation · 6 No basis given · 3 Not checked yet
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Table 3 shows test results on BREAK and MTOP corroborating that EPR substantially improves performance compared to BM25 and CBR.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“BM25 outperforms SBERT for prompt retrieval, and consequently we use BM25 in all of our supervised approaches to retrieve the set of candidates, Ē.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“EPR substantially outperforms all other baselines.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: The candidate set Ē includes good prompt candidates and hard negatives.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“CODEX outperforms GPT-3, which can be explained by the fact that it was trained on code, and our datasets involve mapping to programs or meaning representations.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, accuracy on examples where copying occurred is much higher compared to accuracy where no copying happened.”
§ Learning To Retrieve Prompts for In-Context Learning > Prompt copying
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We test the effect of k, the number of prompts labeled as positive or negative, and L, the number of prompts retrieved by the unsupervised retriever.Table 8 shows that performance is is generally robust w.r.t these hyperparameters.”
- Also stated in: § Additional information · plain
“In the LM-as-a-service setup, using GPT-Neo, we search for other values for L and k, and note that the choice of our hyperparameters is robust.”
§ Learning To Retrieve Prompts for In-Context Learning > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Additional information · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the unsupervised methods, the RANDOM baseline shows that random sampling of training examples leads to poor performance.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the scoring LM and inference LM are identical (using GPT-NEO (Black et al., 2021)), performance compared to the best baseline improves from 26% to 31.9% on BREAK, from 57% to 64.2% on MTOP, and from 51.4% to 54.3% on SMCALFLOW.”
- Also stated in: § Model · plain
“Specifically, when comparing to the best baseline, it improves performance from 26.0 to 31.9 on BREAK, from 57.0 to 64.2 on MTOP, and from 51.4 to 54.3 on SMCALFLOW.”
§ Learning To Retrieve Prompts for In-Context Learning > Similar examples
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Model · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Analogously, when using CODEX as the scoring LM and inference LM performance remains roughly the same: 29.5→29.3.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We evaluate our approach on three sequence-tosequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.”
- Also stated in: § Similar examples · plain
“We observe that EPR substantially improves performance compared to prior work on prompt retrieval.”
§ Learning To Retrieve Prompts for In-Context Learning > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Similar examples · plain
- It may state two separate claims as one. 10
- Probably not a substantial claim. 5
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ANYCORRECT-ORACLE reaches 53.6%, 20 points above LM-ORACLE.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For both, we experimented with passing the retriever the training pair (x, y) or the target sequence y only, and found that using y leads to slightly higher performance.”
§ Learning To Retrieve Prompts for In-Context Learning > 3.1 Generating the Training Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Interestingly, EPR outperforms BM25-ORACLE on MTOP and SMCALFLOW and is comparable on BREAK.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All training examples retrieved by EPR perform an argmax (argmin in the original utterance), and return in the final step”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Zooming in on different inference LMs, GPT-J performs slightly better than GPT-NEO across the board, since it was trained on the same data and using the same procedure as GPT-NEO.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, EPR outperforms the best baseline by 8.5%, and BM25-ORACLE by 5%.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, on MTOP, 84.5% of the examples were abstractly copied, and on that subset of examples, EPR achieves 71.6% EM, compared to 64.2% on the entire validation set.”
§ Learning To Retrieve Prompts for In-Context Learning > Prompt copying
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surprisingly, GPT-J outperforms CODEX (except on MTOP) and GPT-3 despite being 30x smaller.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tables 9, 10, and 11 provide more examples for cases where EPR is correct while CBR is incorrect along with the top-3 prompts for each method.”
- Also stated in: § 6 Conclusions · plain
“An example from MTOP development set where EPR is correct and CBR is incorrect along with the top-3 training examples retrieved from each retriever.”
- Also stated in: § 6 Conclusions · plain
“An example from SMCALFLOW development set where EPR is correct and CBR is incorrect along with the top-3 training examples retrieved from each retriever.”
§ Learning To Retrieve Prompts for In-Context Learning > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 6 Conclusions · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that the rate of copying is much higher in MTOP and SM-CALFLOW compared to BREAK, where in MTOP and SMCALFLOW abstract copying reaches more than 80%.”
§ Learning To Retrieve Prompts for In-Context Learning > Prompt copying
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Large language models have been shown to exhibit various kinds of bias (Bender et al., 2021)”
§ Learning To Retrieve Prompts for In-Context Learning > Additional information
What the sources say
- Not checked yetcites Bender et al., 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since EPR is trained on the signal obtained from such large LMs, it might also exhibit these biases.”
§ Learning To Retrieve Prompts for In-Context Learning > Additional information
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“On BREAK, we evaluate performance on the development set with LF-EM (Hasson and Berant, 2021), which is a better metric compared to Normalized Exact Match (NEM), the official metric, as it measures whether two meaning representations are semantically equivalent.”
§ Learning To Retrieve Prompts for In-Context Learning > 4.3 Experimental Details
What the sources say
- Not checked yetcites Hasson and Berant, 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The performance of LM-ORACLE is substantially higher than EPR, showing that the supervision provided by the scoring LM is strong, and training a better retriever from this signal can substantially enhance performance.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“shows that EPR captures both lexical and structure similarity.”
§ Learning To Retrieve Prompts for In-Context Learning > Model
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
The candidate set Ē includes good prompt candidates and hard negatives.
“This should lead to relevant positive examples, assuming that the set of candidates, Ē includes good prompt candidates and hard negatives, since all candidates have high similarity with (x, y) according to R u (y, D).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although instance-level demonstrations generally perform better than task-level demonstrations, it requires a labeled training set in advance for retrieval.”
From What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? (2023) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Retrieval allows LLMs to access databases, mitigating hallucinations (Li et al. 2022;Wang, Yang, and Wei 2023;Rubin, Herzig, and Berant 2022;Liu et al. 2022).”
From Expel: LLM agents are experiential learners (2024) · cites this paper as Rubin, Herzig, and Berant 2022
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Recent work has demonstrated that retrieval-based mechanisms can improve language models for various tasks requiring factual or procedural knowledge (Liu et al., 2021;Poesia et al., 2021;Rubin et al., 2022;Madaan et al., 2022;Shrivastava et al., 2023).”
From Learning Performance-Improving Code Edits (2023) · cites this paper as Rubin et al., 2022
In the citing paper
“earlier research has showed several positive pedagogical impacts of using NLP-based social conversational agents on children's perception of their own competencies and their attitudes towards learning ([51]).”
From GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills (2024) · cites this paper as [51]
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Research works printed but not linked: 2
- Geoffrey E. Hinton and Sam T. Roweis. 2002. Stochas- tic neighbor embedding. In NIPS.
- Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2021. Nearest Neigh- bor Machine Translation. In Proceedings of ICLR.
Cited by
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Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
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Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv · doi:10.48550/arxiv.2202.12837 · Cited by 752
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Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.759 · Cited by 755
“…2021; Holtzman et al., 2021; Min et al., 2021a), better ways of choosing labeled examples for the demonstrations (Liu et al., 2021; Lu et al., 2021; Rubin et al., 2021), meta-training with an explicit in-context learning objective (Chen et al., 2021; Min et al., 2021b), and learning to follow…”
BackgroundA Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649
Recommender Systems in the Era of Large Language Models (LLMs)
Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., Wen, Z., Wang, F., Zhao, X., Tang, J., et al. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3392335 · Cited by 481
Automatic Chain of Thought Prompting in Large Language Models
Zhang, Z., Zhang, A., Li, M., & Smola, A. (2022) · arXiv · doi:10.48550/arxiv.2210.03493 · Cited by 274
What does a platypus look like? Generating customized prompts for zero-shot image classification
Pratt, S., Covert, I., Liu, R., & Farhadi, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01438 · Cited by 289
Text Classification via Large Language Models
Sun, X., Li, X., Li, J., Wu, F. Q., Guo, S., Zhang, T., & Wang, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.603 · Cited by 376
Lu, G., Ju, X., Chen, X., Pei, W., & Cai, Z. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112031 · Cited by 224
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Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., & Chau, D. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.51 · Cited by 464
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Jiang, X., Dong, Y., Wang, L., Fang, Z., Shang, Q., Li, G., Jin, Z., & Jiao, W. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3672456 · Cited by 158
Expel: LLM agents are experiential learners
Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., & Huang, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29936 · Cited by 153
Reasoning with Language Model Prompting: A Survey
Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.294 · Cited by 346
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Su, H., Shi, W., Kasai, J., Wang, Y., Hu, Y., Mari Ostendorf, E., Yih, W.-T., Smith, N. A., Zettlemoyer, L., & Yu, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.71 · Cited by 190
Ma, Y., Cao, Y., Hong, Y. S., & Sun, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.710 · Cited by 196
GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills
Abdelghani, R., Wang, Y.-H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H., & Oudeyer, P.-Y. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00340-7 · Cited by 129
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Baek, J., Aji, A. F., & Saffari, A. (2023) · NLRSE · doi:10.18653/v1/2023.nlrse-1.7 · Cited by 274
Language Models of Code are Few-Shot Commonsense Learners
Madaan, A., Zhou, S., Alon, U., Yang, Y., & Neubig, G. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.90 · Cited by 191
Deng, Y., Xia, C. S., Yang, C., Zhang, S. D., Yang, S., & Zhang, L. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3623343 · Cited by 120
LLM for SoC Security: A Paradigm Shift
Saha, D., Tarek, S., Yahyaei, K., Saha, S. K., Zhou, J., Tehranipoor, M. M., & Farahmandi, F. (2024) · IEEE Access · doi:10.1109/access.2024.3427369 · Cited by 128
Active Example Selection for In-Context Learning
Zhang, Y., Shi, F., & Tan, C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.622 · Cited by 180
What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?
Gao, S., Wen, X.-C., Gao, C., Wang, W., Zhang, H., & Lyu, M. R. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00109 · Cited by 106
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Huang, X., Lian, J., Lei, Y., Yao, J., Lian, D., & Xie, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3731446 · Cited by 89
Li, J., Liu, Y., Fan, W., Wei, X.-Y., Liu, H., Tang, J., & Li, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3393356 · Cited by 127
DB-GPT: Large Language Model Meets Database
Zhou, X., Sun, Z., & Li, G. (2024) · Data Science and Engineering · doi:10.1007/s41019-023-00235-6 · Cited by 83
ChatGPT as Your Vehicle Co-Pilot: An Initial Attempt
Wang, S., Zhu, Y., Li, Z., Wang, Y., Li, L., & He, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3325300 · Cited by 92
In-context Examples Selection for Machine Translation
Agrawal, S., Zhou, C., Lewis, M., Zettlemoyer, L., & Ghazvininejad, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.564 · Cited by 161
Wu, Z., Wang, Y., Ye, J., & Kong, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.79 · Cited by 158
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3
Hu, Y., Hua, H., Yang, Z., Shi, W., Smith, N. A., & Luo, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00277 · Cited by 194
Making Language Models Better Reasoners with Step-Aware Verifier
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., & Chen, W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.291 · Cited by 69
A Practical Survey on Zero-shot Prompt Design for In-context Learning
Columbia University, N. Y. C., & Li, Y. (2023) · Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Processings · doi:10.26615/978-954-452-092-2_069 · Cited by 68
Open-Vocabulary SAM: Segment and Recognize Twenty-Thousand Classes Interactively
Yuan, H., Li, X., Zhou, C., Li, Y., Chen, K., & Loy, C. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_24 · Cited by 87
Han, B., Susnjak, T., & Mathrani, A. (2024) · Applied Sciences · doi:10.3390/app14199103 · Cited by 68
Cognitive Architectures for Language Agents
Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv · doi:10.48550/arxiv.2309.02427 · Cited by 72
“This can consist of training input-output pairs (Rubin et al., 2021), history event flows (Weston et al.”
BackgroundA Survey of Deep Learning for Mathematical Reasoning
Lu, P., Qiu, L., Yu, W., Welleck, S., & Chang, K.-W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.817 · Cited by 145
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Honovich, O., Shaham, U., Bowman, S. R., & Levy, O. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.108 · Cited by 160
Self-Knowledge Guided Retrieval Augmentation for Large Language Models
Wang, Y., Li, P., Sun, M., & Liu, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.691 · Cited by 141
Diverse Demonstrations Improve In-context Compositional Generalization
Levy, I., Bogin, B., & Berant, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.78 · Cited by 66
Self-citationKnowledge extraction for additive manufacturing process via named entity recognition with LLMs
Liu, X. E., Erkoyuncu, J. A., Fuh, J. Y. H., Lu, W. F., & Li, B. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102900 · Cited by 69
Dolphins: Multimodal Language Model for Driving
Ma, Y., Cao, Y., Sun, J., Pavone, M., & Xiao, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72995-9_23 · Cited by 96
Can We Edit Factual Knowledge by In-Context Learning?
Zheng, C., Li, L., Dong, Q., Fan, Y., Wu, Z., Xu, J., & Chang, B. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.296 · Cited by 54
Prompt-Based Learning for Unpaired Image Captioning
Zhu, P., Wang, X., Zhu, L., Sun, Z., Zheng, W., Wang, Y., & Chen, C. W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3265842 · Cited by 57
Li, J., Li, H., Pan, Z., Sun, D., Wang, J., Zhang, W., & Pan, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.184 · Cited by 65
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Baek, J., Aji, A., & Saffari, A. (2023) · Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023) · doi:10.18653/v1/2023.matching-1.7 · Cited by 51
“To further improve their performances, some work (Rubin et al., 2022; Liu et al., 2022a) proposes retrieving relevant samples to the input question from the training dataset and prepending them in the prompt under few-show learning.”
BackgroundUnified Demonstration Retriever for In-Context Learning
Li, X., Lv, K., Yan, H., Lin, T., Zhu, W., Ni, Y., Xie, G., Wang, X., & Qiu, X. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.256 · Cited by 131
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
Liu, X., Lai, H., Yu, H., Xu, Y., Zeng, A., Du, Z., Zhang, P., Dong, Y., & Tang, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599931 · Cited by 86
Complementary Explanations for Effective In-Context Learning
Ye, X., Iyer, S. V., Çelikyılmaz, A., Stoyanov, V., Durrett, G., & Pasunuru, R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.273 · Cited by 79
Successive Prompting for Decomposing Complex Questions
Dua, D., Gupta, S., Singh, S., & Gardner, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.81 · Cited by 106
Batch Prompting: Efficient Inference with Large Language Model APIs
Cheng, Z., Kasai, J., & Yu, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.74 · Cited by 95
Yang, L., Wang, Z., Li, Z., Na, J., & Yu, J. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103724 · Cited by 46
Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
Fan, M., Han, X., Fan, J., Chai, C., Tang, N., Li, G., & Du, X. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00284 · Cited by 58
Learning In-context Learning for Named Entity Recognition
Chen, J., Lu, Y., Lin, H., Lou, J., Jia, W., Dai, D., Wu, H. R., Cao, B., Han, X., & Sun, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.764 · Cited by 55
Ma, J., Zhao, J., & Hou, Y. (2024) · Sensors · doi:10.3390/s24175502 · Cited by 47
Wu, J., Wu, X., Qiu, Z., Li, M., Lin, S., Zhang, Y., Zheng, Y., Yuan, C., & Yang, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae079 · Cited by 41
Chan, C.-F., Wong, P. K.-Y., Guo, X., Cheng, J. C. P., Chan, J. C. F., Leung, P.-H., & Tao, X. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106305 · Cited by 43
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction
Shui, R., Cao, Y., Wang, X., & Chua, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.490 · Cited by 58
Explainable Knowledge reasoning via thought chains for knowledge-based visual question answering
Qiu, C., Xie, Z., Liu, M., & Hu, H. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103726 · Cited by 34
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Cheng, D., Huang, S., Bi, J., Zhan, Y., Liu, J., Wang, Y., Sun, H., Wei, F., Deng, W., & Zhang, Q. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.758 · Cited by 32
Exploring the Effectiveness of LLMs in Automated Logging Statement Generation: An Empirical Study
Li, Y., Huo, Y., Jiang, Z., Zhong, R., He, P., Su, Y., Briand, L., & Lyu, M. R.-T. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3475375 · Cited by 73
Active Learning Principles for In-Context Learning with Large Language Models
Margatina, K., Schick, T., Αλέτρας, Ν., & Dwivedi-Yu, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.334 · Cited by 72
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Tang, R., Kong, D., Huang, L., & Xue, H. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.284 · Cited by 31
Large Language Models as Evaluators for Recommendation Explanations
Zhang, X., Li, Y., Wang, J., Sun, B., Ma, W., Sun, P., & Zhang, M. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688075 · Cited by 47
Liang, Y., Wang, J., Zhu, H., Wang, L., Qian, W., & Lan, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.263 · Cited by 46
FLTRNN: Faithful Long-Horizon Task Planning for Robotics with Large Language Models
Zhang, J., Tang, L., Song, Y., Meng, Q., Qian, H., Shao, J., Song, W., Zhu, S., & Gu, J. J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611663 · Cited by 32
Finding Support Examples for In-Context Learning
Li, X., & Qiu, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.411 · Cited by 81
Zhao, Z., Zhou, G., Xie, Z., Wu, L., & Huang, J. X. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3658673 · Cited by 29
Patchview: LLM-powered Worldbuilding with Generative Dust and Magnet Visualization
Chung, J. J. Y., & Kreminski, M. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676352 · Cited by 49
Shi, P., Zhang, R., Bai, H., & Lin, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.384 · Cited by 46
Fagbohun, O., Harrison, R. M., & Dereventsov, A. (2023) · Journal of Artificial Intelligence Machine Learning and Data Science · doi:10.51219/jaimld/oluwole-fagbohun/15 · Cited by 61
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Ye, J., Gao, J., Wu, Z., Feng, J., Yu, C., & Kong, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.269 · Cited by 48
Better Zero-Shot Reasoning with Self-Adaptive Prompting
Wan, X., Sun, R., Dai, H., Arık, S. Ö., & Pfister, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.216 · Cited by 56
A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook
Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Qiu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2025) · ACM Computing Surveys · doi:10.1145/3729218 · Cited by 45
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge
Chen, J., Shi, W., Fu, Z., Cheng, S., Li, L., & Xiao, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.550 · Cited by 35
GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models
Rajpoot, P. K., & Parikh, A. P. (2023) · Workshop on Financial Technology and Natural Language Processing (FinNLP) · doi:10.18653/v1/2023.finnlp-2.5 · Cited by 39
What Makes Multimodal In-Context Learning Work?
Baldassini, F. B., Shukor, M., Cord, M., Soulier, L., & Piwowarski, B. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00161 · Cited by 35
Exploring Effective Factors for Improving Visual In-Context Learning
Sun, Y., Chen, Q., Wang, J., Wang, J., & Li, Z. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3554410 · Cited by 61
In-Context Learning for Text Classification with Many Labels
Milios, A., Reddy, S., & Bahdanau, D. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.14 · Cited by 33
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
Ye, S., Hwang, H., Yang, S., Yun, H., Kim, Y., & Seo, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29909 · Cited by 59
ScatterShot: Interactive In-context Example Curation for Text Transformation
Wu, T., Shen, H., Weld, D. S., Heer, J., & Ribeiro, M. T. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584059 · Cited by 34
Mishra, B. D., Tafjord, O., & Clark, P. E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.644 · Cited by 42
MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning
Zhang, C., Lin, K., Yang, Z., Wang, J., Li, L., Lin, C.-C., Liu, Z., & Wang, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01295 · Cited by 24
Learning Performance-Improving Code Edits
Shypula, A., Madaan, A., Zeng, Y., Alon, U., Gardner, J., Hashemi, M., Neubig, G., Ranganathan, P., Bastani, O., & Yazdanbakhsh, A. (2023) · arXiv · doi:10.48550/arxiv.2302.07867 · Cited by 146
“Recent work has demonstrated that retrieval-based mechanisms can improve language models for various tasks requiring factual or procedural knowledge (Liu et al., 2021; Poesia et al., 2021; Rubin et al., 2022; Madaan et al., 2022; Shrivastava et al., 2023).”
BackgroundHow Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?
Lo, L. Y., & Qu, H. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456333 · Cited by 39
How Effective are Large Language Models in Generating Software Specifications?
Xie, D., Yoo, B., Jiang, N., Kim, M., Tan, L., Zhang, X., & Lee, J. S. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00014 · Cited by 77
Shan, T., Zhang, F., Chan, A. P. C., Zhu, S., & Li, K. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103655 · Cited by 24
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve
Li, X., & Qiu, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.392 · Cited by 53
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Qiu, L., Shaw, P. M., Pasupat, P., Shi, T., Herzig, J., Pitler, E., Sha, F., & Toutanova, K. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.624 · Cited by 41
Self-citationPre-Training to Learn in Context
Gu, Y., Dong, L., Wei, F., & Huang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.267 · Cited by 51
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
Yu, Y., Zhuang, Y., Zhang, R., Yu, M., Shen, J., & Zhang, C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.748 · Cited by 28
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Yuan, X., Wang, T., Wang, Y.-H., Fine, E., Abdelghani, R., Sauzéon, H., & Oudeyer, P.-Y. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.820 · Cited by 21
Boosting Your Context by Dual Similarity Checkup for In-Context Learning Medical Image Segmentation
Gao, J. Y., Lao, Q., Kang, Q., Liu, P., Du, C., Li, K., & Zhang, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3440311 · Cited by 22
Retrieval-style In-context Learning for Few-shot Hierarchical Text Classification
Chen, H., Zhao, Y. H., Chen, Z., Wang, M., Li, L., Zhang, M., & Zhang, M. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00697 · Cited by 23
Multi-stage guided code generation for Large Language Models
Han, Y., & Lyu, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109491 · Cited by 25
In-context Learning for Few-shot Multimodal Named Entity Recognition
Cai, C., Wang, Q., Liang, B., Qin, B., Yang, M., Wong, K., & Xu, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.196 · Cited by 24
Dr.ICL: Demonstration-Retrieved In-context Learning
Luo, M., Xu, X., Dai, Z., Pasupat, P., Kazemi, M., Baral, C. R., Imbrasaitė, V., & Zhao, V. Y. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0012 · Cited by 86
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
Chen, K., Du, Y., You, T., Islam, M., Guo, Z., Jin, Y., Chen, G., & Heng, P. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610603 · Cited by 21
Managing Linux servers with LLM-based AI agents: An empirical evaluation with GPT4
Cao, Q., Wang, F., Lindley, L. C., & Wang, Z. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100570 · Cited by 28
Enhancing named entity recognition with external knowledge from large language model
Li, Q., Xie, T., Zhang, J., Ma, K., Su, J., Yang, K., & Wang, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113471 · Cited by 22
How Do In-Context Examples Affect Compositional Generalization?
An, S., Lin, Z., Fu, Q., Chen, B., Zheng, N., Lou, J., & Zhang, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.618 · Cited by 46
Metadata
- Authors
- Rubin, O., Herzig, J., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 2655-2671
- OpenAlex
- W4287891464
- Semantic Scholar
- f9838a3be5c94bb2674a0e224de349b50e18f3c4
- Cited by
- 374
- References
- 54
- Citations by year
- 2026: 57 · 2025: 97 · 2024: 91 · 2023: 119 · 2022: 10
- FWCI
- 43.96
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- in-context learning, dense retrievers, training data selection, pre-trained language models, annotated dataset, meaning representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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