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    <abstract>Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature of real-world facts, the answer can be completely different when the time constraint in the question changes. Recently, Large Language Models (LLMs) have shown remarkable intelligence in question answering, while our experiments reveal that the aforementioned problems still pose a significant challenge to existing LLMs. This can be attributed to the LLMs’ inability to perform rigorous reasoning based on surface-level text semantics. To overcome this limitation, rather than requiring LLMs to directly answer the question, we propose a novel approach where we reframe the Question Answering task as Programming (QAaP). Concretely, by leveraging modern LLMs’ superior capability in understanding both natural language and programming language, we endeavor to harness LLMs to represent diversely expressed text as well-structured code and select the best matching answer from multiple candidates through programming. We evaluate our QAaP framework on several time-sensitive question answering datasets and achieve decent improvement, up to 14.5% over strong baselines.</abstract>
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%0 Conference Proceedings
%T Question Answering as Programming for Solving Time-Sensitive Questions
%A Zhu, Xinyu
%A Yang, Cheng
%A Chen, Bei
%A Li, Siheng
%A Lou, Jian-Guang
%A Yang, Yujiu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-question
%X Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature of real-world facts, the answer can be completely different when the time constraint in the question changes. Recently, Large Language Models (LLMs) have shown remarkable intelligence in question answering, while our experiments reveal that the aforementioned problems still pose a significant challenge to existing LLMs. This can be attributed to the LLMs’ inability to perform rigorous reasoning based on surface-level text semantics. To overcome this limitation, rather than requiring LLMs to directly answer the question, we propose a novel approach where we reframe the Question Answering task as Programming (QAaP). Concretely, by leveraging modern LLMs’ superior capability in understanding both natural language and programming language, we endeavor to harness LLMs to represent diversely expressed text as well-structured code and select the best matching answer from multiple candidates through programming. We evaluate our QAaP framework on several time-sensitive question answering datasets and achieve decent improvement, up to 14.5% over strong baselines.
%R 10.18653/v1/2023.emnlp-main.787
%U https://aclanthology.org/2023.emnlp-main.787
%U https://doi.org/10.18653/v1/2023.emnlp-main.787
%P 12775-12790

Markdown (Informal)

[Question Answering as Programming for Solving Time-Sensitive Questions](https://aclanthology.org/2023.emnlp-main.787) (Zhu et al., EMNLP 2023)

• Question Answering as Programming for Solving Time-Sensitive Questions (Zhu et al., EMNLP 2023)

ACL

• Xinyu Zhu, Cheng Yang, Bei Chen, Siheng Li, Jian-Guang Lou, and Yujiu Yang. 2023. Question Answering as Programming for Solving Time-Sensitive Questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12775–12790, Singapore. Association for Computational Linguistics.