Publication: Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
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Schimanski, T., Ni, J., Kraus, M., Ash, E., & Leippold, M. (2024). Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 1913–1931. https://doi.org/10.18653/v1/2024.acl-long.105
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Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is basing the answers on reliable sources. However, this Evidence-Based QA has proven to work insufficiently with LLMs in terms of citing the correct sources (source quality) and truthfully representing the information within sources (answer attributability). In this work, we systematically investigate how to robustly fine-tune LLMs for better source quality and
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- Schimanski, Tobias
- Ni, Jingwei
- Kraus, Mathias
- Ash, Elliott
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Schimanski, T., Ni, J., Kraus, M., Ash, E., & Leippold, M. (2024). Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 1913–1931. https://doi.org/10.18653/v1/2024.acl-long.105
