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A curated collection of research papers on Large Language Models (LLMs), transformers, prompting, fine-tuning, and multi-modal systems. Maintained by Sean Moran.

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Dialogue Natural Language Inference

Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 253 citations

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ACL Datasets Evaluation

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model’s consistency.

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