Measuring and Narrowing the Compositionality Gap in Language Models

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 · see citing works

Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101 · see citing works

Large Language Models are Zero-Shot Reasoners

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · arXiv · doi:10.48550/arxiv.2205.11916 · Cited by 2,013 · see citing works

Large language models are zero-shot reasoners

Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013 · see citing works

A Diversity-Promoting Objective Function for Neural Conversation Models

Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1014 · Cited by 1,981 · see citing works

A Neural Conversational Model

Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works

Emergent Abilities of Large Language Models

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · see citing works

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177 · see citing works

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Izacard, G., & Grave, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.74 · Cited by 1,109 · see citing works

Neural Responding Machine for Short-Text Conversation

Shang, L., Lu, Z., & Li, H. (2015) · doi:10.3115/v1/p15-1152 · Cited by 1,009 · see citing works

Decomposed Prompting: A Modular Approach for Solving Complex TasksPreprint

Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., & Sabharwal, A. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2210.02406 · Cited by 852 · see citing works

Recipes for Building an Open-Domain Chatbot

Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., et al. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.24 · Cited by 784 · see citing works

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.08239 · Cited by 778 · see citing works

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J., Gao, J., & Dolan, B. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1020 · Cited by 768 · see citing works

SkipNet: Learning Dynamic Routing in Convolutional Networks

Wang, X., Yu, F., Dou, Z.-Y., Darrell, T., & Gonzalez, J. E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_25 · Cited by 643 · see citing works

How Much Knowledge Can You Pack into the Parameters of a Language Model?

Roberts, A., Raffel, C., & Shazeer, N. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.437 · Cited by 638 · see citing works

Multi-Scale Dense Networks for Resource Efficient Image ClassificationPreprint

Huang, G. M., Chen, D., Li, T., Wu, F., van der Maaten, L., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09844 · Cited by 638 · see citing works

Towards a Human-like Open-Domain Chatbot

Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614 · see citing works

ReAct: Synergizing Reasoning and Acting in Language Models

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022) · arXiv · doi:10.48550/arxiv.2210.03629 · Cited by 608 · see citing works

REALM: Retrieval-Augmented Language Model Pre-Training

Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020) · arXiv · doi:10.48550/arxiv.2002.08909 · Cited by 574 · see citing works

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569 · see citing works

The Web as a Knowledge-Base for Answering Complex Questions

Talmor, A., & Berant, J. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1059 · Cited by 520 · see citing works

Improving language models by retrieving from trillions of tokens

Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04426 · Cited by 382 · see citing works

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.10625 · Cited by 380 · see citing works

The Second Conversational Intelligence Challenge (ConvAI2)

Dinan, E., Logacheva, V., Malykh, V., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A. D., Serban, I. V., Lowe, R., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_7 · Cited by 362 · see citing works

The Second Conversational Intelligence Challenge (ConvAI2)

Dinan, E., Logacheva, V., Malykh, V., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A., Serban, I., Lowe, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.00098 · Cited by 355 · see citing works

Adaptive Computation Time for Recurrent Neural Networks

Graves, A. (2016) · arXiv · doi:10.48550/arxiv.1603.08983 · Cited by 335 · see citing works

Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1015 · Cited by 325 · see citing works

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287 · see citing works

Generalization through Memorization: Nearest Neighbor Language Models

Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2019) · arXiv · doi:10.48550/arxiv.1911.00172 · Cited by 285 · see citing works

♫ MuSiQue: Multihop Questions via Single-hop Question Composition

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00475 · Cited by 283 · see citing works

WebGPT: Browser-assisted question-answering with human feedback

Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.09332 · Cited by 272 · see citing works

oLMpics-On What Language Model Pre-training Captures

Talmor, A., Elazar, Y., Goldberg, Y., & Berant, J. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00342 · Cited by 267 · see citing works

Adaptive Neural Networks for Efficient Inference

Bolukbasi, T., Wang, J., Dekel, O., & Saligrama, V. (2017) · arXiv · doi:10.48550/arxiv.1702.07811 · Cited by 251 · see citing works

Search-based Neural Structured Learning for Sequential Question Answering

Iyyer, M., Yih, W.-T., & Chang, M. (2017) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p17-1167 · Cited by 219 · see citing works

Measuring Compositional Generalization: A Comprehensive Method on Realistic DataPreprint

Keysers, D., Schärli, N., Scales, N., Buisman, H., Furrer, D., Kashubin, S., Momchev, N., Sinopalnikov, D., Stafiniak, Ł., Tihon, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.09713 · Cited by 213 · see citing works

Neural Responding Machine for Short-Text ConversationPreprint

Shang, L., Lu, Z., & Li, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.02364 · Cited by 212 · see citing works

Generated Knowledge Prompting for Commonsense Reasoning

Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Bras, R. J. L., Choi, Y., & Hajishirzi, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.225 · Cited by 206 · see citing works

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1613 · Cited by 197 · see citing works

Compositionality Decomposed: How do Neural Networks Generalise?

Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11674 · Cited by 194 · see citing works

Unsupervised Commonsense Question Answering with Self-Talk

Shwartz, V., West, P., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.373 · Cited by 186 · see citing works

Show Your Work: Scratchpads for Intermediate Computation with Language Models

Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.00114 · Cited by 144 · see citing works

Reframing Instructional Prompts to GPTk’s Language

Mishra, S. R., Khashabi, D., Baral, C. R., Choi, Y., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.50 · Cited by 140 · see citing works

The Right Tool for the Job: Matching Model and Instance Complexities

Schwartz, R., Stanovsky, G., Swayamdipta, S., Dodge, J., & Smith, N. A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.593 · Cited by 136 · see citing works

Ask the Right Questions: Active Question Reformulation with Reinforcement LearningPreprint

Buck, C., Bulian, J., Ciaramita, M., Gajewski, W., Gesmundo, A., Houlsby, N., & Wang, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07830 · Cited by 127 · see citing works

Unsupervised Question Decomposition for Question Answering

Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.713 · Cited by 123 · see citing works

Neural Logic MachinesPreprint

Dong, H., Mao, J., Lin, T., Wang, C., Li, L. C., & Zhou, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11694 · Cited by 123 · see citing works

Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image RecognitionPreprint

Wang, Y., Huang, R., Song, S., Huang, Z., & Huang, G. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.15075 · Cited by 101 · see citing works

Answering Complex Open-domain Questions Through Iterative Query Generation

Qi, P., Lin, X., Mehr, L., Wang, Z., & Manning, C. D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1261 · Cited by 96 · see citing works

Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networksPreprint

Lake, B., & Baroni, M. (2017) · International Conference on Learning Representations · doi:10.48550/arxiv.1711.00350 · Cited by 85 · see citing works

Generative Deep Neural Networks for Dialogue: A Short ReviewPreprint

Serban, I. V., Lowe, R., Charlin, L., & Pineau, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06216 · Cited by 82 · see citing works

Neural Speed Reading via Skim-RNNPreprint

Seo, M., Min, S., Farhadi, A., & Hajishirzi, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02085 · Cited by 69 · see citing works

Is a Question Decomposition Unit All We Need?Preprint

Patel, P. H., Mishra, S., Parmar, M., & Baral, C. (2022) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2205.12538 · Cited by 67 · see citing works

Knowledge-Aware Language Model PretrainingPreprint

Rosset, C., Xiong, C., Phan, M., Song, X., Bennett, P. N., & Tiwary, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00655 · Cited by 60 · see citing works

Teaching language models to support answers with verified quotes

Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., Glaese, M., Young, S., Campbell-Gillingham, L., Irving, G., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.11147 · Cited by 53 · see citing works

RealTime QA: What's the Answer Right Now?

Kasai, J., Sakaguchi, K., Takahashi, Y., Bras, R. L., Asai, A., Yu, X., Radev, D., Smith, N. A., Choi, Y., & Inui, K. (2022) · arXiv · doi:10.48550/arxiv.2207.13332 · Cited by 52 · see citing works

RealTime QA: What's the Answer Right Now?

Kasai, J., Sakaguchi, K., Takahashi, Y., Bras, R. L., Asai, A., Yu, X., Radev, D., Smith, N., Choi, Y., & Inui, K. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2130 · Cited by 52 · see citing works

Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models

Khot, T., Khashabi, D., Richardson, K., Clark, P. E., & Sabharwal, A. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.99 · Cited by 48 · see citing works

SkipNet: Learning Dynamic Routing in Convolutional NetworksPreprint

Wang, X., Yu, F., Dou, Z.-Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09485 · Cited by 46 · see citing works

Break It Down: A Question Understanding BenchmarkPreprint

Wolfson, T., Geva, M., Gupta, A., Gardner, M., Goldberg, Y., Deutch, D., & Berant, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11770 · Cited by 40 · see citing works

The Unreliability of Explanations in Few-Shot In-Context LearningPreprint

Ye, X., & Durrett, G. (2022) · arXiv.org · doi:10.48550/arxiv.2205.03401 · Cited by 37 · see citing works

Answer-based Adversarial Training for Generating Clarification Questions

Rao, S., & Daumé, H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1013 · Cited by 34 · see citing works

Is a Question Decomposition Unit All We Need?

Patel, P., Mishra, S. R., Parmar, M., & Baral, C. R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.302 · Cited by 25 · see citing works

Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2Preprint

Betz, G., Richardson, K., & Voigt, C. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13033 · Cited by 23 · see citing works

Multi-hop Reading Comprehension through Question Decomposition and RescoringPreprint

Min, S., Zhong, V., Zettlemoyer, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02916 · Cited by 14 · see citing works

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent NetworksPreprint

Schwarzschild, A., Borgnia, E., Gupta, A. K., Huang, F., Vishkin, U., Goldblum, M., & Goldstein, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04537 · Cited by 11 · see citing works

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works

Augmenting large language models with chemistry tools

M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794 · see citing works

Unleashing the potential of prompt engineering for large language models

CHEN, B. ‐., Zhang, Z., Langrené, N., & Zhu, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101260 · Cited by 764 · see citing works

Active Retrieval Augmented Generation

Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.495 · Cited by 497 · see citing works

Towards Reasoning in Large Language Models: A Survey

Huang, J., & Chang, K. C.-C. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.67 · Cited by 428 · see citing works

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · arXiv · doi:10.48550/arxiv.2212.10509 · Cited by 349 · see citing works

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.557 · Cited by 349 · see citing works

Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., & Lim, E.-P. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.147 · Cited by 344 · see citing works

Query Rewriting in Retrieval-Augmented Large Language Models

Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.322 · Cited by 391 · see citing works

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Yang, J., Jimenez, C., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-1601 · Cited by 264 · see citing works

Large Language Models for Information Retrieval: A Survey

Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., & Wen, J.-R. (2026) · ACM Transactions on Information Systems · doi:10.1145/3748304 · Cited by 225 · see citing works

Enabling Large Language Models to Generate Text with Citations

Gao, T., Yen, H., Yu, J., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.398 · Cited by 216 · see citing works

Query2doc: Query Expansion with Large Language Models

Wang, L., Yang, N., & Wei, F. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.585 · Cited by 214 · see citing works

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., & Chen, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.620 · Cited by 200 · see citing works

Human-like systematic generalization through a meta-learning neural network

Lake, B. M., & Baroni, M. (2023) · Nature · doi:10.1038/s41586-023-06668-3 · Cited by 194 · see citing works

Untitled work

(2024) · doi:10.1145/3704435 · Cited by 184 · see citing works

Tool Learning with Foundation Models

Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.08354 · Cited by 184 · see citing works

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

Xi, Y., Liu, W., Lin, J., Cai, X., Zhu, H. C., Zhu, J., Chen, B., Tang, R., Zhang, W., & Yu, Y. H. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688104 · Cited by 246 · see citing works

Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Zhang, W., & Zhang, J. (2025) · Mathematics · doi:10.3390/math13050856 · Cited by 145 · see citing works

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 218 · see citing works

TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Hu, Y., Liu, B., Kasai, J., Wang, Y., Mari Ostendorf, E., Krishna, R., & Smith, N. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01866 · Cited by 151 · see citing works

Bridging the Gulf of Envisioning: Cognitive Challenges in Prompt Based Interactions with LLMs

Subramonyam, H., Pea, R. D., Pondoc, C., Agrawala, M., & Seifert, C. M. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642754 · Cited by 169 · see citing works

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

Wang, B., Min, S., Deng, X., Shen, J., Wu, Y., Zettlemoyer, L., & Sun, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.153 · Cited by 120 · see citing works

Language Model Behavior: A Comprehensive Survey

Chang, T. A., & Bergen, B. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00492 · Cited by 105 · see citing works

The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination

Barman, D., Guo, Z., & Conlan, O. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100545 · Cited by 103 · see citing works

DB-GPT: Large Language Model Meets Database

Zhou, X.-H., Sun, Z., & Li, G.-L. (2024) · Data Science and Engineering · doi:10.1007/s41019-023-00235-6 · Cited by 135 · see citing works

Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., & Elazar, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.779 · Cited by 103 · see citing works

Limitations of large language models in clinical problem-solving arising from inflexible reasoning

Kim, J., Podlasek, A., Shidara, K., Liu, F., Alaa, A., & Bernardo, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-22940-0 · Cited by 78 · see citing works

Large models for intelligent transportation systems and autonomous vehicles: A survey

Gan, L., Chu, W., Li, G., Tang, X., & Li, K. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102786 · Cited by 79 · see citing works

From Matching to Generation: A Survey on Generative Information Retrieval

Li, X., Jin, J., Zhou, Y., Zhang, Y., Zhang, P., Zhu, Y., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3722552 · Cited by 124 · see citing works

Synthetic Data Generation Using Large Language Models: Advances in Text and Code

Nadas, M. D., Dioşan, L. S., & Tomescu, A. (2025) · IEEE Access · doi:10.1109/access.2025.3589503 · Cited by 99 · see citing works

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 88 · see citing works

Natural Language Reasoning, A Survey

Yu, F., Zhang, H., Tiwari, P., & Wang, B. (2024) · ACM Computing Surveys · doi:10.1145/3664194 · Cited by 78 · see citing works

Fact-Checking Complex Claims with Program-Guided Reasoning

Pan, L., Wu, X., Lu, X., Luu, A. T., Wang, W. Y., Kan, M., & Nakov, P. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.386 · Cited by 78 · see citing works

LM vs LM: Detecting Factual Errors via Cross Examination

Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.778 · Cited by 60 · see citing works

Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework

Zhao, R., Li, X., Joty, S., Qin, C., & Bing, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.320 · Cited by 59 · see citing works

Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

Zhang, X., & Gao, W. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.64 · Cited by 90 · see citing works

Large Language Model Instruction Following: A Survey of Progresses and Challenges

Lou, R., Zhang, K., & Yin, W. (2023) · Computational Linguistics · doi:10.1162/coli_a_00523 · Cited by 74 · see citing works

Untitled work

(2023) · doi:10.18653/v1/2023.emnlp-main.971 · Cited by 55 · see citing works

From language to action: a review of large language models as autonomous agents and tool users

Chowa, S. S., Alvi, R., Rahman, S. S., Rahman, M. A., Raiaan, M. A. K., Islam, M. R., Hussain, M., & Azam, S. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11471-9 · Cited by 50 · see citing works

AutoWebGLM: A Large Language Model-based Web Navigating Agent

Lai, H., Liu, X., Iong, I. L., Yao, S., Chen, Y., Shen, P., Yu, H., Zhang, H., Zhang, X., Dong, Y., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671620 · Cited by 69 · see citing works

Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies

Nan, L., Zhao, Y., Zou, W., Ri, N., Tae, J., Zhang, E., Cohan, A., & Radev, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.996 · Cited by 69 · see citing works

ThoughtSource: A central hub for large language model reasoning data

Ott, S., Hebenstreit, K., Liévin, V., Hother, C., Moradi, M., Mayrhauser, M., Praas, R., Winther, O., & Samwald, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02433-3 · Cited by 52 · see citing works

TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks

Santu, S. K. K., & Feng, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.946 · Cited by 47 · see citing works

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 55 · see citing works

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 46 · see citing works

IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

You, H., Sun, R., Wang, Z., Chen, L., Wang, G., Ayyubi, H., Chang, K.-W., & Chang, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.755 · Cited by 55 · see citing works

Design and evaluation of an Autonomous Cyber Defence agent using DRL and an augmented LLM

Loevenich, J. F., Adler, E., Hürten, T., & Lopes, R. R. F. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111162 · Cited by 52 · see citing works

Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

Wang, H., & Shu, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.416 · Cited by 49 · see citing works

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Wang, H., Wang, R., Mi, F., Deng, Y., Wang, Z., Liang, B., Xu, R., & Wong, K.-F. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.806 · Cited by 38 · see citing works

Answering Questions by Meta-Reasoning over Multiple Chains of Thought

Yoran, O., Wolfson, T., Bogin, B., Katz, U., Deutch, D., & Berant, J. (2023) · arXiv · doi:10.48550/arxiv.2304.13007 · Cited by 38 · see citing works

Untitled work

(2023) · doi:10.18653/v1/2023.emnlp-main.364 · Cited by 38 · see citing works

Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks

Xu, S., Pang, L., Shen, H., Cheng, X., & Chua, T. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645363 · Cited by 59 · see citing works

The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"

Berglund, L., Tong, M., Kaufmann, M., Balesni, M., Stickland, A. C., Korbak, T., & Evans, O. (2023) · arXiv · doi:10.48550/arxiv.2309.12288 · Cited by 36 · see citing works

Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

Cheng, Y., Chen, J., Huang, Q. Y., Xing, Z., Xu, X., & Lu, Q. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3638247 · Cited by 35 · see citing works

From ideas to ventures: building entrepreneurship knowledge with LLM, prompt engineering, and conversational agents

Thanasi-Boçe, M., & Hoxha, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12775-z · Cited by 38 · see citing works

Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities EmergencePreprint

Nay, J. J., Karamardian, D., Lawsky, S. B., Tao, W., Bhat, M., Jain, R., Lee, A. T., Choi, J. H., & Kasai, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4476325 · Cited by 32 · see citing works

Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams

Subramonyam, H., Thakkar, D., Ku, A., Dieber, J., & Sinha, A. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713166 · Cited by 38 · see citing works

The argument for near-term human disempowerment through AI

Dung, L. (2024) · AI & Society · doi:10.1007/s00146-024-01930-2 · Cited by 29 · see citing works

Performance analysis of large language models in the domain of legal argument mining

Zubaer, A. A., Granitzer, M., & Mitrović, J. (2023) · Frontiers Artif. Intell. · doi:10.3389/frai.2023.1278796 · Cited by 44 · see citing works

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

Zhang, Z., Fang, M., Chen, L., Namazi‐Rad, M., & Wang, Y.-X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.516 · Cited by 37 · see citing works

Visconde: Multi-document QA with GPT-3 and Neural Reranking

Pereira, J., Fidalgo, R., Lotufo, R., & Nogueira, R. F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28238-6_44 · Cited by 27 · see citing works

Leveraging Large Language Models for Automatic Smart Contract Generation

Napoli, E. A., Barbàra, F., Gatteschi, V., & Schifanella, C. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00100 · Cited by 24 · see citing works

Exploring Effective Factors for Improving Visual In-Context Learning

Sun, Y.-P., Chen, Q., Wang, J., Wang, J.-D., & Li, Z.-C. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3554410 · Cited by 73 · see citing works

FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

Chern, I.-C., Chern, S., Chen, S., Yuan, W., Feng, K., Zhou, C., He, J., Neubig, G., & Liu, P. (2023) · arXiv · doi:10.48550/arxiv.2307.13528 · Cited by 29 · see citing works

Modular Visual Question Answering via Code Generation

Subramanian, S., Narasimhan, M., Khangaonkar, K., Yang, K., Nagrani, A., Schmid, C., Zeng, A., Darrell, T., & Klein, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.65 · Cited by 28 · see citing works

An active inference strategy for prompting reliable responses from large language models in medical practice

Shusterman, R., Waters, A. C., O’Neill, S. K., Bangs, M., Luu, P., & Tucker, D. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01516-2 · Cited by 22 · see citing works

LoCal: Logical and Causal Fact-Checking with LLM-Based Multi-Agents

Ma, J., Hu, L., Li, R., & Fu, W. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714748 · Cited by 24 · see citing works

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 36 · see citing works

FIT-RAG: Black-Box RAG with Factual Information and Token Reduction

Mao, Y., Dong, X., Xu, W., Gao, Y., Wei, B., & Zhang, Y. (2024) · ACM Transactions on Information Systems · doi:10.1145/3676957 · Cited by 29 · see citing works

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Gutiérrez, B. J., Shu, Y., Gu, Y., Yasunaga, M., & Su, Y. (2024) · arXiv · doi:10.48550/arxiv.2405.14831 · Cited by 152 · see citing works

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 21 · see citing works

Demystifying Chains, Trees, and Graphs of Thoughts

Besta, M., Memedi, F., Zhang, Z., Gerstenberger, R., Piao, G., Blach, N., Nyczyk, P., Copik, M., Kwaśniewski, G., Müller, J., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3598182 · Cited by 34 · see citing works

Prompting Language Models for Linguistic Structure

Blevins, T., Gonen, H., & Zettlemoyer, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.367 · Cited by 25 · see citing works

Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning

Russin, J., Pavlick, E., & Frank, M. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2510270122 · Cited by 23 · see citing works

SciSciGPT: advancing human–AI collaboration in the science of science

Shao, E., Wang, Y., Qian, Y., Pan, Z., Liu, H., & Wang, D. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00906-6 · Cited by 26 · see citing works

A Survey of Reasoning with Foundation ModelsPreprint

Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Liu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2023) · doi:10.31219/osf.io/ac4sp · Cited by 51 · see citing works

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions

Cao, S., Zhang, J., Shi, J., Lv, X., Yao, Z., Tian, Q., Hou, L., & Li, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.835 · Cited by 17 · see citing works

Instruct Me More! Random Prompting for Visual In-Context Learning

Zhang, J., Wang, B.-W., Li, L.-Z., Nakashima, Y., & Nagahara, H. (2023) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00258 · Cited by 42 · see citing works

Promises and Pitfalls: Using Large Language Models to Generate Visualization Items

Cui, Y., Ge, L. W., Ding, Y., Harrison, L., Yang, F., & Kay, M. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456309 · Cited by 17 · see citing works

Layered Query Retrieval: An Adaptive Framework for Retrieval-Augmented Generation in Complex Question Answering for Large Language Models

Huang, J., Wang, M., Cui, Y., Liu, J., Chen, L., Wang, T., Li, H., & Wu, J. (2024) · Applied Sciences · doi:10.3390/app142311014 · Cited by 17 · see citing works

Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models

Verga, P., Hofstatter, S., Althammer, S., Su, Y., Piktus, A., Arkhangorodsky, A., Xu, M., White, N., & Lewis, P. (2024) · arXiv · doi:10.48550/arxiv.2404.18796 · Cited by 16 · see citing works

Interacting with Large Language Models: A Case Study on AI-Aided Brainstorming for Guesstimation Problems

Salikutluk, V., Koert, D., & Jäkel, F. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230081 · Cited by 15 · see citing works

ContextMate: a context-aware smart agent for efficient data analysis

Jadoon, A. K., Yu, C., & Shi, Y.-C. (2024) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-023-00144-7 · Cited by 15 · see citing works

Learning by thinking in natural and artificial minds

Lombrozo, T. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.07.007 · Cited by 14 · see citing works

Structured knowledge-based causal discovery: Agentic streams of thought

Meier, S., Raut, P. N., Mahr, F., Thielen, N., Franke, J. E., & Risch, F. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104202 · Cited by 13 · see citing works

FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

Feng, S., Balachandran, V., Bai, Y., & Tsvetkov, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.59 · Cited by 21 · see citing works

SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables

Lu, X., Pan, L., Liu, Q., Nakov, P., & Kan, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.483 · Cited by 13 · see citing works

ChatLLM network: More brains, more intelligence

Hao, R., Hu, L., Qi, W., Wu, Q., Zhang, Y., & Nie, L. (2025) · AI Open · doi:10.1016/j.aiopen.2025.01.001 · Cited by 29 · see citing works

OpenICL: An Open-Source Framework for In-context Learning

Wu, Z., Wang, Y., Ye, J., Wu, Z., Feng, J., Xu, J., & Qiao, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.47 · Cited by 13 · see citing works

The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective

Kim, J., Shin, B., Chung, J., & Rhu, M. (2026) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca68181.2026.11408569 · Cited by 11 · see citing works

Modeling Collaborator: Enabling Subjective Vision Classification with Minimal Human Effort via LLM Tool-Use

Toubal, I. E., Avinash, A., Alldrin, N., Dlabal, J., Zhou, W., Luo, E., Stretcu, O., Xiong, H., Lu, C.-T., Zhou, H., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01662 · Cited by 12 · see citing works

SiReRAG: Indexing Similar and Related Information for Multihop ReasoningPreprint

Zhang, N., Choubey, P. K., Fabbri, A. R., Bernadett-Shapiro, G., Zhang, R., Mitra, P., Xiong, C., & Wu, C.-S. (2024) · Qeios · doi:10.32388/93o2s3 · Cited by 17 · see citing works

Beamaggr: Beam aggregation reasoning over multi-source knowledge for multi-hop question answering

Chu, Z., Chen, J., Chen, Q., Wang, H., Zhu, K., Du, X., Yu, W., Liu, M., & Qin, B. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.67 · Cited by 11 · see citing works

What Algorithms can Transformers Learn? A Study in Length Generalization

Zhou, H., Bradley, A., Littwin, E., Razin, N., Saremi, O., Susskind, J., Bengio, S., & Nakkiran, P. (2023) · arXiv · doi:10.48550/arxiv.2310.16028 · Cited by 10 · see citing works

Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment

Zhao, B., Zhang, Y., Xu, Z., Ren, Y., Zhang, X., Luo, R., Feng, Z., & Xia, F. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761103 · Cited by 12 · see citing works

Evaluating LLM-based Agents for Multi-turn Conversations: A Survey

Guan, S., Wang, J., 渡辺, 将., Zhu, B., Lou, J., & Xiong, H. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3793671 · Cited by 28 · see citing works

FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline

Singh, K., Kaur, S., & Smiley, C. (2024) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3677052.3698682 · Cited by 11 · see citing works

PestGPT: Leveraging Large Language Models and IoT for Timely and Customized Recommendation Generation in Sustainable Pest Management

Yuan, Z., Liu, K., Peng, R., Li, S., Leybourne, D. J., Musa, N., Huang, H., & Yang, P. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2400036 · Cited by 11 · see citing works

Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering

Nair, I., Somasundaram, S., Saxena, A., & Goswami, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.972 · Cited by 11 · see citing works

Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries

Biran, E., Gottesman, D., Yang, S., Geva, M., & Globerson, A. (2024) · arXiv · doi:10.48550/arxiv.2406.12775

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

Karmim, Y., Martí, L., Seddah, D., & Barrière, V. (2026) · HAL

Do Large Language Models Latently Perform Multi-Hop Reasoning?

Yang, S., Gribovskaya, E., Kassner, N., Geva, M., & Riedel, S. (2024) · arXiv · doi:10.48550/arxiv.2402.16837

Teaching Smaller Language Models To Generalise To Unseen Compositional QuestionsPreprint

Hartill, T., Tan, N. Ö., Witbrock, M., & Riddle, P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.00946

Evaluating Compositionality of Vision and Language Models

McCord, G. (2020)