Compositional Exemplars for In-context Learning

Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:39818-39833, 2023.

Abstract

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task simply by conditioning on a prompt consisting of input-output examples as demonstration, without any parameter updates. The performance of ICL is highly dominated by the quality of the selected in-context examples. However, previous selection methods are mostly based on simple heuristics, leading to sub-optimal performance. In this work, we systematically formulate in-context example selection as a subset selection problem, and optimize it in an end-to-end fashion. We propose CEIL (Compositional Exemplars for In-context Learning), which is instantiated by Determinantal Point Processes (DPPs) to model the interaction between the given input and in-context examples, and optimized through carefully-designed contrastive learning to obtain preference from LMs. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, phraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation and semantic parsing. Extensive experiments demonstrate the effectiveness, transferability, compositionality of CEIL, shedding new lights on in-context leaning. Our code is released at https://github.com/HKUNLP/icl-ceil.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-ye23c, title = {Compositional Exemplars for In-context Learning}, author = {Ye, Jiacheng and Wu, Zhiyong and Feng, Jiangtao and Yu, Tao and Kong, Lingpeng}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {39818--39833}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/ye23c/ye23c.pdf}, url = {https://proceedings.mlr.press/v202/ye23c.html}, abstract = {Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task simply by conditioning on a prompt consisting of input-output examples as demonstration, without any parameter updates. The performance of ICL is highly dominated by the quality of the selected in-context examples. However, previous selection methods are mostly based on simple heuristics, leading to sub-optimal performance. In this work, we systematically formulate in-context example selection as a subset selection problem, and optimize it in an end-to-end fashion. We propose CEIL (Compositional Exemplars for In-context Learning), which is instantiated by Determinantal Point Processes (DPPs) to model the interaction between the given input and in-context examples, and optimized through carefully-designed contrastive learning to obtain preference from LMs. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, phraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation and semantic parsing. Extensive experiments demonstrate the effectiveness, transferability, compositionality of CEIL, shedding new lights on in-context leaning. Our code is released at https://github.com/HKUNLP/icl-ceil.} }
Endnote
%0 Conference Paper %T Compositional Exemplars for In-context Learning %A Jiacheng Ye %A Zhiyong Wu %A Jiangtao Feng %A Tao Yu %A Lingpeng Kong %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-ye23c %I PMLR %P 39818--39833 %U https://proceedings.mlr.press/v202/ye23c.html %V 202 %X Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task simply by conditioning on a prompt consisting of input-output examples as demonstration, without any parameter updates. The performance of ICL is highly dominated by the quality of the selected in-context examples. However, previous selection methods are mostly based on simple heuristics, leading to sub-optimal performance. In this work, we systematically formulate in-context example selection as a subset selection problem, and optimize it in an end-to-end fashion. We propose CEIL (Compositional Exemplars for In-context Learning), which is instantiated by Determinantal Point Processes (DPPs) to model the interaction between the given input and in-context examples, and optimized through carefully-designed contrastive learning to obtain preference from LMs. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, phraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation and semantic parsing. Extensive experiments demonstrate the effectiveness, transferability, compositionality of CEIL, shedding new lights on in-context leaning. Our code is released at https://github.com/HKUNLP/icl-ceil.
APA
Ye, J., Wu, Z., Feng, J., Yu, T. & Kong, L.. (2023). Compositional Exemplars for In-context Learning. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:39818-39833 Available from https://proceedings.mlr.press/v202/ye23c.html.

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