Roberta Raileanu


2024

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Chain-of-Verification Reduces Hallucination in Large Language Models
Shehzaad Dhuliawala | Mojtaba Komeili | Jing Xu | Roberta Raileanu | Xian Li | Asli Celikyilmaz | Jason Weston
Findings of the Association for Computational Linguistics ACL 2024

Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation.

2023

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AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Tuan Dung Nguyen | Yuan-Sen Ting | Ioana Ciuca | Charles O’Neill | Ze-Chang Sun | Maja Jabłońska | Sandor Kruk | Ernest Perkowski | Jack Miller | Jason Jason Jingsh Li | Josh Peek | Kartheik Iyer | Tomasz Rozanski | Pranav Khetarpal | Sharaf Zaman | David Brodrick | Sergio J. Rodriguez Mendez | Thang Bui | Alyssa Goodman | Alberto Accomazzi | Jill Naiman | Jesse Cranney | Kevin Schawinski | Roberta Raileanu
Proceedings of the Second Workshop on Information Extraction from Scientific Publications