Sachdeva, Rachneet ; Tutek, Martin ; Gurevych, Iryna (2024)
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration.
18th Conference of the European Chapter of the Association for Computational Linguistics. St. Julian's, Malta (17-22.03.2024)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. In our work, we investigate the use of LLMs to augment training data of smaller language models (SLMs) with automatically generated counterfactual (CF) instances – i.e. minimally altered inputs – in order to improve out-of-domain (OOD) performance of SLMs in the extractive question answering (QA) setup. We show that, across various LLM generators, such data augmentation consistently enhances OOD performance and improves model calibration for both confidence-based and rationale-augmented calibrator models. Furthermore, these performance improvements correlate with higher diversity of CF instances in terms of their surface form and semantic content. Finally, we show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance, indicating that rationale-augmented calibrators prefer concise explanations.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Sachdeva, Rachneet ; Tutek, Martin ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration |
| Sprache: | Englisch |
| Publikationsjahr: | März 2024 |
| Verlag: | ACL |
| Buchtitel: | Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) |
| Veranstaltungstitel: | 18th Conference of the European Chapter of the Association for Computational Linguistics |
| Veranstaltungsort: | St. Julian's, Malta |
| Veranstaltungsdatum: | 17-22.03.2024 |
| URL / URN: | https://aclanthology.org/2024.eacl-long.113 |
| Kurzbeschreibung (Abstract): | In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. In our work, we investigate the use of LLMs to augment training data of smaller language models (SLMs) with automatically generated counterfactual (CF) instances – i.e. minimally altered inputs – in order to improve out-of-domain (OOD) performance of SLMs in the extractive question answering (QA) setup. We show that, across various LLM generators, such data augmentation consistently enhances OOD performance and improves model calibration for both confidence-based and rationale-augmented calibrator models. Furthermore, these performance improvements correlate with higher diversity of CF instances in terms of their surface form and semantic content. Finally, we show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance, indicating that rationale-augmented calibrators prefer concise explanations. |
| Freie Schlagworte: | UKP_p_square,UKP_p_InterText, UKP_p_seditrah_factcheck |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 23 Apr 2024 08:50 |
| Letzte Änderung: | 23 Apr 2024 08:50 |
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