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    <abstract>Large language models (LLMs) are capable to perform complex reasoning by in-context learning (ICL) when provided with a few input-output demonstrations (demos) and more powerful when intermediate reasoning steps (chain of thoughts (CoT)) of the demos are given. Is it necessary to use multi-demo in ICL? In this paper, we study ICL using fewer demos for each test query on the tasks in (Wei et al., 2022). Surprisingly, we do not observe significant degradation when using only one randomly chosen demo. To study this phenomenon, for each test query, we categorize demos into “positive demos” leading to the correct answer, and “negative demos” resulting in wrong answers. Our analysis reveals an inherent bias in those widely studied datasets and the redundancy of demos: most demos are positive for a majority of test queries, which explains the good performance of ICL with one random demo. Moreover, ICL (with and w/o CoT) using only one positive demo significantly outperforms multi-demo ICL adopted by most previous works, indicating the weakness of LLMs in finding positive demo(s) for input queries, which is difficult to evaluate on the biased datasets. Furthermore, we observe a counterintuitive behavior of ICL using multi-demo, i.e., its accuracy degrades(improves) when given more positive(negative) demos. This implies that ICL can be easily misguided by interference among demos and their spurious correlations. Our analyses highlight several fundamental challenges that need to be addressed in LLMs training, ICL, and benchmark design.</abstract>
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%0 Conference Proceedings
%T How Many Demonstrations Do You Need for In-context Learning?
%A Chen, Jiuhai
%A Chen, Lichang
%A Zhu, Chen
%A Zhou, Tianyi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-many
%X Large language models (LLMs) are capable to perform complex reasoning by in-context learning (ICL) when provided with a few input-output demonstrations (demos) and more powerful when intermediate reasoning steps (chain of thoughts (CoT)) of the demos are given. Is it necessary to use multi-demo in ICL? In this paper, we study ICL using fewer demos for each test query on the tasks in (Wei et al., 2022). Surprisingly, we do not observe significant degradation when using only one randomly chosen demo. To study this phenomenon, for each test query, we categorize demos into “positive demos” leading to the correct answer, and “negative demos” resulting in wrong answers. Our analysis reveals an inherent bias in those widely studied datasets and the redundancy of demos: most demos are positive for a majority of test queries, which explains the good performance of ICL with one random demo. Moreover, ICL (with and w/o CoT) using only one positive demo significantly outperforms multi-demo ICL adopted by most previous works, indicating the weakness of LLMs in finding positive demo(s) for input queries, which is difficult to evaluate on the biased datasets. Furthermore, we observe a counterintuitive behavior of ICL using multi-demo, i.e., its accuracy degrades(improves) when given more positive(negative) demos. This implies that ICL can be easily misguided by interference among demos and their spurious correlations. Our analyses highlight several fundamental challenges that need to be addressed in LLMs training, ICL, and benchmark design.
%R 10.18653/v1/2023.findings-emnlp.745
%U https://aclanthology.org/2023.findings-emnlp.745
%U https://doi.org/10.18653/v1/2023.findings-emnlp.745
%P 11149-11159

Markdown (Informal)

[How Many Demonstrations Do You Need for In-context Learning?](https://aclanthology.org/2023.findings-emnlp.745) (Chen et al., Findings 2023)

• How Many Demonstrations Do You Need for In-context Learning? (Chen et al., Findings 2023)

ACL

• Jiuhai Chen, Lichang Chen, Chen Zhu, and Tianyi Zhou. 2023. How Many Demonstrations Do You Need for In-context Learning?. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11149–11159, Singapore. Association for Computational Linguistics.