@inproceedings{kassner-etal-2023-language,
title = "Language Models with Rationality",
author = "Kassner, Nora and
Tafjord, Oyvind and
Sabharwal, Ashish and
Richardson, Kyle and
Schuetze, Hinrich and
Clark, Peter",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.877",
doi = "10.18653/v1/2023.emnlp-main.877",
pages = "14190--14201",
abstract = "While large language models (LLMs) are proficient at question-answering (QA), it is not always clear how (or even if) an answer follows from their latent {``}beliefs{''}. This lack of interpretability is a growing impediment to widespread use of LLMs. To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs. Our approach, which we call REFLEX, is to add a **rational, self-reflecting layer** on top of the LLM. First, given a question, we construct a **belief graph** using a backward-chaining process to materialize relevant model beliefs (including beliefs about answer candidates) and their inferential relationships. Second, we identify and minimize contradictions in that graph using a formal constraint reasoner. We find that REFLEX significantly improves consistency (by 8{\%}-11{\%} absolute) without harming overall answer accuracy, resulting in answers supported by faithful chains of reasoning drawn from a more consistent belief system. This suggests a new style of system architecture in which an LLM extended with a rational layer can provide an interpretable window into system beliefs, add a systematic reasoning capability, and repair latent inconsistencies present in the LLM.",
}
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<abstract>While large language models (LLMs) are proficient at question-answering (QA), it is not always clear how (or even if) an answer follows from their latent “beliefs”. This lack of interpretability is a growing impediment to widespread use of LLMs. To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs. Our approach, which we call REFLEX, is to add a **rational, self-reflecting layer** on top of the LLM. First, given a question, we construct a **belief graph** using a backward-chaining process to materialize relevant model beliefs (including beliefs about answer candidates) and their inferential relationships. Second, we identify and minimize contradictions in that graph using a formal constraint reasoner. We find that REFLEX significantly improves consistency (by 8%-11% absolute) without harming overall answer accuracy, resulting in answers supported by faithful chains of reasoning drawn from a more consistent belief system. This suggests a new style of system architecture in which an LLM extended with a rational layer can provide an interpretable window into system beliefs, add a systematic reasoning capability, and repair latent inconsistencies present in the LLM.</abstract>
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%0 Conference Proceedings
%T Language Models with Rationality
%A Kassner, Nora
%A Tafjord, Oyvind
%A Sabharwal, Ashish
%A Richardson, Kyle
%A Schuetze, Hinrich
%A Clark, Peter
%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 kassner-etal-2023-language
%X While large language models (LLMs) are proficient at question-answering (QA), it is not always clear how (or even if) an answer follows from their latent “beliefs”. This lack of interpretability is a growing impediment to widespread use of LLMs. To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs. Our approach, which we call REFLEX, is to add a **rational, self-reflecting layer** on top of the LLM. First, given a question, we construct a **belief graph** using a backward-chaining process to materialize relevant model beliefs (including beliefs about answer candidates) and their inferential relationships. Second, we identify and minimize contradictions in that graph using a formal constraint reasoner. We find that REFLEX significantly improves consistency (by 8%-11% absolute) without harming overall answer accuracy, resulting in answers supported by faithful chains of reasoning drawn from a more consistent belief system. This suggests a new style of system architecture in which an LLM extended with a rational layer can provide an interpretable window into system beliefs, add a systematic reasoning capability, and repair latent inconsistencies present in the LLM.
%R 10.18653/v1/2023.emnlp-main.877
%U https://aclanthology.org/2023.emnlp-main.877
%U https://doi.org/10.18653/v1/2023.emnlp-main.877
%P 14190-14201
Markdown (Informal)
[Language Models with Rationality](https://aclanthology.org/2023.emnlp-main.877) (Kassner et al., EMNLP 2023)
ACL
- Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schuetze, and Peter Clark. 2023. Language Models with Rationality. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14190–14201, Singapore. Association for Computational Linguistics.