The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
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In the citing paper
“To address these issues, Mekala et al. [2024] introduced ECHO, a selfharmonized prompting framework that unifies diverse reasoning paths into a coherent pattern, balancing automation with robustness.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Mekala et al., 2024
In the citing paper
“It retained performance with 50% fewer examples, showing only a -0.8% dip compared to Few-Shot-CoT's -1.3% decline.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Mekala et al., 2024
In the citing paper
“ECHO surpassed Auto-CoT by an average of 2.8% across 10 reasoning benchmarks (arithmetic, commonsense, symbolic) while demonstrating greater efficiency.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Mekala et al., 2024
In the citing paper
“The method also achieved 2.3% gains over Auto-CoT in Mixtral-8x7B, though it remained behind GPT-3.5, a gap attributed to differences in the quality of reasoning rationales.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Mekala et al., 2024
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Citations
Cites
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. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
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
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022) · arXiv · doi:10.48550/arxiv.2205.11822 · Cited by 98
Cited by
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024) · arXiv · doi:10.48550/arxiv.2402.07927 · Cited by 288
Metadata
- Authors
- Mekala, R. S. R., Razeghi, Y., & Singh, S.
- Venue
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Published
- 2024
- Pages
- 399-432
- Cited by
- 5
- References
- 4
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning. https://app.cochat.ai/papers/10.18653/v1/2024.naacl-short.35
- The paper
- Mekala, R. S. R., Razeghi, Y., & Singh, S. (2024). EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). https://doi.org/10.18653/v1/2024.naacl-short.35
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
