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Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-like levels.
Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 2021–2031 (2017)
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Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. pp. 4171–4186 (2019)
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
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Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite bert for self-supervised learning of language representations. In: ICLR (2020), https://openreview.net/forum?id=H1eA7AEtvS
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Liu, J., Cui, L., Liu, H., Huang, D., Wang, Y., Zhang, Y.: Logiqa: A challenge dataset for machine reading comprehension with logical reasoning. In: Bessiere, C. (ed.) IJCAI 2020. pp. 3622–3628. ijcai.org (2020). https://doi.org/10.24963/ijcai.2020/501, https://doi.org/10.24963/ijcai.2020/501
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Liu, K., Liu, X., Yang, A., Liu, J., Su, J., Li, S., She, Q.: A robust adversarial training approach to machine reading comprehension. In: AAAI. vol. 34, pp. 8392–8400 (2020)
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Yu, W., Jiang, Z., Dong, Y., Feng, J.: Reclor: A reading comprehension dataset requiring logical reasoning. In: 8th ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net (2020), https://openreview.net/forum?id=HJgJtT4tvB
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Clark, P., Tafjord, O., Richardson, K.: Transformers as soft reasoners over language. In: IJCAI. pp. 3882–3890 (2021)
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He, P., Liu, X., Gao, J., Chen, W.: Deberta: decoding-enhanced bert with disentangled attention. In: ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net (2021), https://openreview.net/forum?id=XPZIaotutsD
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Bao, Q., Peng, A.Y., Hartill, T., Tan, N., Deng, Z., Witbrock, M., Liu, J.: Multi-step deductive reasoning over natural language: An empirical study on out-of-distribution generalisation. In: Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 2nd International Joint Conference on Learning & Reasoning (IJCLR 2022). pp. 202–217. Cumberland Lodge, Windsor Great Park, United Kingdom (Sep 2022)
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Jiao, F., Guo, Y., Song, X., Nie, L.: MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning. In: Findings of ACL 2022. pp. 3496–3509. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.findings-acl.276, https://aclanthology.org/2022.findings-acl.276
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Li, J., Tang, T., Gong, Z., Yang, L., Yu, Z., Chen, Z., Wang, J., Zhao, W.X., Wen, J.R.: Eliteplm: An empirical study on general language ability evaluation of pretrained language models. In: NAACL 2022. pp. 3519–3539 (2022)
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Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality (March 2023), https://lmsys.org/blog/2023-03-30-vicuna/
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Liu, H., Liu, J., Cui, L., Teng, Z., Duan, N., Zhou, M., Zhang, Y.: Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing 31
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2023
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Mishra, S., Khashabi, D., Baral, C., Hajishirzi, H.: Cross-task generalization via natural language crowdsourcing instructions. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 3470–3487. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-long.244, https://aclanthology.org/2022.acl-long.244
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Sanyal, S., Liao, Z., Ren, X.: Robustlr: A diagnostic benchmark for evaluating logical robustness of deductive reasoners. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 9614–9631 (2022)
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Wang, S., Zhong, W., Tang, D., Wei, Z., Fan, Z., Jiang, D., Zhou, M., Duan, N.: Logic-driven context extension and data augmentation for logical reasoning of text. In: Findings of ACL 2022. pp. 1619–1629. ACL (2022). https://doi.org/10.18653/v1/2022.findings-acl.127, https://aclanthology.org/2022.findings-acl.127
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Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., Le, Q.V.: Finetuned language models are zero-shot learners. In: ICLR (2022), https://openreview.net/forum?id=gEZrGCozdqR
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2022
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter, Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain of thought prompting elicits reasoning in large language models. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022), https://openreview.net/forum?id=_VjQlMeSB_J
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Young, N., Bao, Q., Bensemann, J., Witbrock, M.: AbductionRules: Training transformers to explain unexpected inputs. In: Findings of ACL 2022. pp. 218–227. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.findings-acl.19, https://aclanthology.org/2022.findings-acl.19
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OpenAI: Chatgpt: Optimizing language models for dialogue. (2023), https://openai.com/blog/chatgpt
2023
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OpenAI: Gpt-4 technical report (2023)
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Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca (2023)
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Xu, Z., Yang, Z., Cui, Y., Wang, S.: IDOL: Indicator-oriented logic pre-training for logical reasoning. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Findings of ACL 2023. pp. 8099–8111. ACL (Jul 2023). https://doi.org/10.18653/v1/2023.findings-acl.513, https://aclanthology.org/2023.findings-acl.513
2023
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Bao, Q., Peng, A., Deng, Z., Zhong, W., Gendron, G., Pistotti, T., Tan, N., Young, N., Chen, Y., Zhu, Y., Denny, P., Witbrock, M., Liu, J.: Abstract Meaning Representation-based logic-driven data augmentation for logical reasoning. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Findings of ACL 2024. pp. 5914–5934 (Aug 2024), https://aclanthology.org/2024.findings-acl.353
2024
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