@inproceedings{stacey-etal-2024-atomic,
title = "Atomic Inference for {NLI} with Generated Facts as Atoms",
author = "Stacey, Joe and
Minervini, Pasquale and
Dubossarsky, Haim and
Camburu, Oana-Maria and
Rei, Marek",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.569/",
doi = "10.18653/v1/2024.emnlp-main.569",
pages = "10188--10204",
abstract = "With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models are faithful, i.e. that they reflect the inner workings of the model. Atomic inference overcomes this issue, providing interpretable and faithful model decisions. This approach involves making predictions for different components (or atoms) of an instance, before using interpretable and deterministic rules to derive the overall prediction based on the individual atom-level predictions. We investigate the effectiveness of using LLM-generated facts as atoms, decomposing Natural Language Inference premises into lists of facts. While directly using generated facts in atomic inference systems can result in worse performance, with 1) a multi-stage fact generation process, and 2) a training regime that incorporates the facts, our fact-based method outperforms other approaches."
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<abstract>With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models are faithful, i.e. that they reflect the inner workings of the model. Atomic inference overcomes this issue, providing interpretable and faithful model decisions. This approach involves making predictions for different components (or atoms) of an instance, before using interpretable and deterministic rules to derive the overall prediction based on the individual atom-level predictions. We investigate the effectiveness of using LLM-generated facts as atoms, decomposing Natural Language Inference premises into lists of facts. While directly using generated facts in atomic inference systems can result in worse performance, with 1) a multi-stage fact generation process, and 2) a training regime that incorporates the facts, our fact-based method outperforms other approaches.</abstract>
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%0 Conference Proceedings
%T Atomic Inference for NLI with Generated Facts as Atoms
%A Stacey, Joe
%A Minervini, Pasquale
%A Dubossarsky, Haim
%A Camburu, Oana-Maria
%A Rei, Marek
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F stacey-etal-2024-atomic
%X With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models are faithful, i.e. that they reflect the inner workings of the model. Atomic inference overcomes this issue, providing interpretable and faithful model decisions. This approach involves making predictions for different components (or atoms) of an instance, before using interpretable and deterministic rules to derive the overall prediction based on the individual atom-level predictions. We investigate the effectiveness of using LLM-generated facts as atoms, decomposing Natural Language Inference premises into lists of facts. While directly using generated facts in atomic inference systems can result in worse performance, with 1) a multi-stage fact generation process, and 2) a training regime that incorporates the facts, our fact-based method outperforms other approaches.
%R 10.18653/v1/2024.emnlp-main.569
%U https://aclanthology.org/2024.emnlp-main.569/
%U https://doi.org/10.18653/v1/2024.emnlp-main.569
%P 10188-10204
Markdown (Informal)
[Atomic Inference for NLI with Generated Facts as Atoms](https://aclanthology.org/2024.emnlp-main.569/) (Stacey et al., EMNLP 2024)
ACL
- Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Oana-Maria Camburu, and Marek Rei. 2024. Atomic Inference for NLI with Generated Facts as Atoms. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10188–10204, Miami, Florida, USA. Association for Computational Linguistics.