Built on

N. J. Nilsson, Principles of artificial intelligence . Springer, 1982
1982
Earlier work this paper cites.
S. Franklin and A. Graesser, “Is it an agent, or just a program?: A taxonomy for autonomous agents,” in International workshop on agent theories, architectures, and languages . Springer, 1996, pp. 21–35
1996
Earlier work this paper cites.
E. M. Voorhees and D. M. Tice, “The trec-8 question answering track report,” in Text retrieval conference TREC . Citeseer, 1999
1999
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S. Robertson, “Understanding inverse document frequency: on theoretical arguments for idf,” Journal of documentation , vol. 60, no. 5, pp. 503–520, 2004
2004
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C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
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C. Carpineto and G. Romano, “A survey of automatic query expansion in information retrieval,” ACM Computing Surveys (CSUR) , vol. 44, no. 1, pp. 1–50, 2012
2012
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D. Bahdanau et al. , “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
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V. Mnih et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
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T. P. Lillicrap et al. , “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015
Original
2015
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Y. Yang, W.-t. Yih, and C. Meek, “Wikiqa: A challenge dataset for open-domain question answering using wikipedia,” arXiv preprint arXiv:1412.7808 , 2015
Original
2015
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P. Rajpurkar et al. , “Squad: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, pp. 2383–2392
2016
Earlier work this paper cites.
M. Iyyer, W.-t. Yih, and M.-W. Chang, “Search-based neural structured learning for sequential question answering,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2017, pp. 1821–1831
2017
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J. Devlin et al. , “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
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Z. Yang, P. Qi, S. Zhang, Y. Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning, “Hotpotqa: A dataset for diverse, explainable multi-hop question answering,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 2369–2380
2018
Earlier work this paper cites.
S. Garg et al. , “Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 5488–5494
2019
Earlier work this paper cites.
F. Petroni et al. , “Language models as knowledge bases?” arXiv preprint arXiv:1909.01066 , 2019
Original
2019
Earlier work this paper cites.
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,” arXiv preprint arXiv:1903.00161 , 2019
Original
2019
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A. Fan, P. Lewis, and Y. Dauphin, “Eli5: Long-form question answering,” arXiv preprint arXiv:1907.09190 , 2019
Original
2019
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Q. Chen, Z. Zhuo, and W. Wang, “Bert for joint intent classification and slot filling,” arXiv preprint arXiv:1902.10909 , 2019
Original
2019
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” arXiv preprint arXiv:1904.09675 , 2019
Original
2019
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V. Karpukhin et al. , “Dense passage retrieval for open-domain question answering,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6769–6781
2020
Earlier work this paper cites.
P. Lewis et al. , “Retrieval-augmented generation for knowledge-intensive nlp tasks,” in Advances in Neural Information Processing Systems , 2020, pp. 9459–9474
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” arXiv preprint arXiv:2009.03300 , 2020
Original
2020
Earlier work this paper cites.
F. Zhu, W. Lei, C. Wang, J. Zheng, S. Poria, and T.-S. Chua, “Retrieving and reading: A comprehensive survey on open-domain question answering,” arXiv preprint arXiv:2101.00774 , 2021
Original
2021
Earlier work this paper cites.
M. Geva, D. Khashabi, E. Segal, T. Khot, D. Roth, and J. Berant, “Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 346–361, 2021
2021
Earlier work this paper cites.
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, C. Hesse, and J. Schulman, “Training verifiers to solve math word problems,” arXiv preprint arXiv:2110.14168 , 2021
Original
2021
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the math dataset,” arXiv preprint arXiv:2103.03874 , 2021
Original
2021
Earlier work this paper cites.
I. Stelmakh, Y. Luan, B. Dhingra, and M.-W. Chang, “Asqa: Factoid questions meet long-form answers,” arXiv preprint arXiv:2204.06092 , 2022
Original
2022
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M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le, E. H. Chi, D. Zhou, , and J. Wei, “Challenging big-bench tasks and whether chain-of-thought can solve them,” arXiv preprint arXiv:2210.09261 , 2022
Original
2022
Earlier work this paper cites.
S. Han, H. Schoelkopf, Y. Zhao, Z. Qi, M. Riddell, L. Benson, L. Sun, E. Zubova, Y. Qiao, M. Burtell, D. Peng, J. Fan, Y. Liu, B. Wong, M. Sailor, A. Ni, L. Nan, J. Kasai, T. Yu, R. Zhang, S. Joty, A. R. Fabbri, W. Kryscinski, X. V. Lin, C. Xiong, and D. Radev, “Folio: Natural language reasoning with first-order logic,” arXiv preprint arXiv:2209.00840 , 2022. [Online]. Available: https://arxiv.org/abs/2209.00840
Original
2022
Earlier work this paper cites.

Similar

S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
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2022
Cited alongside, same era.
W. Chen, X. Ma, X. Wang, and W. W. Cohen, “Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks,” arXiv preprint arXiv:2211.12588 , 2022
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Cited alongside, same era.
Z. Cheng, T. Xie, P. Shi, C. Li, R. Nadkarni, Y. Hu, C. Xiong, D. Radev, M. Ostendorf, L. Zettlemoyer et al. , “Binding language models in symbolic languages,” arXiv preprint arXiv:2210.02875 , 2022
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2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.

Then

X. Chen, M. Lin, N. Schärli, and D. Zhou, “Teaching large language models to self-debug,” arXiv preprint arXiv:2304.05128 , 2023
Original
2023
Later among the works it cites.
X. Wang, C. Li, Z. Wang, F. Bai, H. Luo, J. Zhang, N. Jojic, E. P. Xing, and Z. Hu, “Promptagent: Strategic planning with language models enables expert-level prompt optimization,” arXiv preprint arXiv:2310.16427 , 2023
Original
2023
Later among the works it cites.
X. Wang, Z. Wang, J. Liu, Y. Chen, L. Yuan, H. Peng, and H. Ji, “Mint: Evaluating llms in multi-turn interaction with tools and language feedback,” arXiv preprint arXiv:2309.10691 , 2023
Original
2023
Later among the works it cites.
H. Yan, S. Srivastava, Y. Tai, S. I. Wang, W.-t. Yih, and Z. Yao, “Learning to simulate natural language feedback for interactive semantic parsing,” arXiv preprint arXiv:2305.08195 , 2023
Original
2023
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2025

A Survey of Large Language Model Agents for Question Answering

Yue, Murong

Understand

This paper surveys the development of large language model (LLM)-based agents for question answering (QA).

Traditional agents face significant limitations, including substantial data requirements and difficulty in generalizing to new environments.
LLM-based agents address these challenges by leveraging LLMs as their core reasoning engine.
These agents achieve superior QA results compared to traditional QA pipelines and naive LLM QA systems by enabling interaction with external environments.

Built on

N. J. Nilsson, Principles of artificial intelligence . Springer, 1982
1982
Earlier work this paper cites.
S. Franklin and A. Graesser, “Is it an agent, or just a program?: A taxonomy for autonomous agents,” in International workshop on agent theories, architectures, and languages . Springer, 1996, pp. 21–35
1996
Earlier work this paper cites.
E. M. Voorhees and D. M. Tice, “The trec-8 question answering track report,” in Text retrieval conference TREC . Citeseer, 1999
1999
Earlier work this paper cites.
S. Robertson, “Understanding inverse document frequency: on theoretical arguments for idf,” Journal of documentation , vol. 60, no. 5, pp. 503–520, 2004
2004
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
C. Carpineto and G. Romano, “A survey of automatic query expansion in information retrieval,” ACM Computing Surveys (CSUR) , vol. 44, no. 1, pp. 1–50, 2012
2012
Earlier work this paper cites.
D. Bahdanau et al. , “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
Earlier work this paper cites.
V. Mnih et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
T. P. Lillicrap et al. , “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015
Original
2015
Earlier work this paper cites.
Y. Yang, W.-t. Yih, and C. Meek, “Wikiqa: A challenge dataset for open-domain question answering using wikipedia,” arXiv preprint arXiv:1412.7808 , 2015
Original
2015
Earlier work this paper cites.
P. Rajpurkar et al. , “Squad: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, pp. 2383–2392
2016
Earlier work this paper cites.
M. Iyyer, W.-t. Yih, and M.-W. Chang, “Search-based neural structured learning for sequential question answering,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2017, pp. 1821–1831
2017
Earlier work this paper cites.
J. Devlin et al. , “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
Z. Yang, P. Qi, S. Zhang, Y. Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning, “Hotpotqa: A dataset for diverse, explainable multi-hop question answering,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 2369–2380
2018
Earlier work this paper cites.
S. Garg et al. , “Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 5488–5494
2019
Earlier work this paper cites.
F. Petroni et al. , “Language models as knowledge bases?” arXiv preprint arXiv:1909.01066 , 2019
Original
2019
Earlier work this paper cites.
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,” arXiv preprint arXiv:1903.00161 , 2019
Original
2019
Earlier work this paper cites.
A. Fan, P. Lewis, and Y. Dauphin, “Eli5: Long-form question answering,” arXiv preprint arXiv:1907.09190 , 2019
Original
2019
Earlier work this paper cites.
Q. Chen, Z. Zhuo, and W. Wang, “Bert for joint intent classification and slot filling,” arXiv preprint arXiv:1902.10909 , 2019
Original
2019
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” arXiv preprint arXiv:1904.09675 , 2019
Original
2019
Earlier work this paper cites.
V. Karpukhin et al. , “Dense passage retrieval for open-domain question answering,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6769–6781
2020
Earlier work this paper cites.
P. Lewis et al. , “Retrieval-augmented generation for knowledge-intensive nlp tasks,” in Advances in Neural Information Processing Systems , 2020, pp. 9459–9474
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” arXiv preprint arXiv:2009.03300 , 2020
Original
2020
Earlier work this paper cites.
F. Zhu, W. Lei, C. Wang, J. Zheng, S. Poria, and T.-S. Chua, “Retrieving and reading: A comprehensive survey on open-domain question answering,” arXiv preprint arXiv:2101.00774 , 2021
Original
2021
Earlier work this paper cites.
M. Geva, D. Khashabi, E. Segal, T. Khot, D. Roth, and J. Berant, “Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 346–361, 2021
2021
Earlier work this paper cites.
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, C. Hesse, and J. Schulman, “Training verifiers to solve math word problems,” arXiv preprint arXiv:2110.14168 , 2021
Original
2021
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the math dataset,” arXiv preprint arXiv:2103.03874 , 2021
Original
2021
Earlier work this paper cites.
I. Stelmakh, Y. Luan, B. Dhingra, and M.-W. Chang, “Asqa: Factoid questions meet long-form answers,” arXiv preprint arXiv:2204.06092 , 2022
Original
2022
Earlier work this paper cites.
M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le, E. H. Chi, D. Zhou, , and J. Wei, “Challenging big-bench tasks and whether chain-of-thought can solve them,” arXiv preprint arXiv:2210.09261 , 2022
Original
2022
Earlier work this paper cites.
S. Han, H. Schoelkopf, Y. Zhao, Z. Qi, M. Riddell, L. Benson, L. Sun, E. Zubova, Y. Qiao, M. Burtell, D. Peng, J. Fan, Y. Liu, B. Wong, M. Sailor, A. Ni, L. Nan, J. Kasai, T. Yu, R. Zhang, S. Joty, A. R. Fabbri, W. Kryscinski, X. V. Lin, C. Xiong, and D. Radev, “Folio: Natural language reasoning with first-order logic,” arXiv preprint arXiv:2209.00840 , 2022. [Online]. Available: https://arxiv.org/abs/2209.00840
Original
2022
Earlier work this paper cites.

Similar

S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629 , 2022
Original
2022
Cited alongside, same era.
W. Chen, X. Ma, X. Wang, and W. W. Cohen, “Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks,” arXiv preprint arXiv:2211.12588 , 2022
Original
2022
Cited alongside, same era.
Z. Cheng, T. Xie, P. Shi, C. Li, R. Nadkarni, Y. Hu, C. Xiong, D. Radev, M. Ostendorf, L. Zettlemoyer et al. , “Binding language models in symbolic languages,” arXiv preprint arXiv:2210.02875 , 2022
Original
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.

Then

X. Chen, M. Lin, N. Schärli, and D. Zhou, “Teaching large language models to self-debug,” arXiv preprint arXiv:2304.05128 , 2023
Original
2023
Later among the works it cites.
X. Wang, C. Li, Z. Wang, F. Bai, H. Luo, J. Zhang, N. Jojic, E. P. Xing, and Z. Hu, “Promptagent: Strategic planning with language models enables expert-level prompt optimization,” arXiv preprint arXiv:2310.16427 , 2023
Original
2023
Later among the works it cites.
X. Wang, Z. Wang, J. Liu, Y. Chen, L. Yuan, H. Peng, and H. Ji, “Mint: Evaluating llms in multi-turn interaction with tools and language feedback,” arXiv preprint arXiv:2309.10691 , 2023
Original
2023
Later among the works it cites.
H. Yan, S. Srivastava, Y. Tai, S. I. Wang, W.-t. Yih, and Z. Yao, “Learning to simulate natural language feedback for interactive semantic parsing,” arXiv preprint arXiv:2305.08195 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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T. Schick, J. Dwivedi-Yu, Z. Jiang, F. Petroni, P. Lewis, G. Izacard, Q. You, C. Nalmpantis, E. Grave, and S. Riedel, “Peer: A collaborative language model,” arXiv preprint arXiv:2208.11663 , 2022

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OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

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Cited alongside, same era.

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W. Chen, M. Yin, M. Ku, P. Lu, Y. Wan, X. Ma, J. Xu, X. Wang, and T. Xia, “Theoremqa: A theorem-driven question answering dataset,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 7889–7901

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W. Yu, M. Jiang, P. Clark, and A. Sabharwal, “Ifqa: A dataset for open-domain question answering under counterfactual presuppositions,” arXiv preprint arXiv:2305.14010 , 2023

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S. J. Semnani, V. Z. Yao, H. C. Zhang, and M. S. Lam, “Wikichat: Stopping the hallucination of large language model chatbots by few-shot grounding on wikipedia,” arXiv preprint arXiv:2305.14292 , 2023

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2023