Papers by Roberta Raileanu
Chain-of-Verification Reduces Hallucination in Large Language Models (2024.findings-acl)
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Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston
| Challenge: | Large language models can generate plausible but incorrect factual information, termed hallucinations, but they can still fail on lesser known facts. |
| Approach: | They develop a method that allows language models to deliberate on the responses they give in order to correct their errors. |
| Outcome: | The proposed method decreases hallucinations across a variety of tasks, including list-based questions, closed book MultiSpanQA and longform text generation. |
TOOLVERIFIER: Generalization to New Tools via Self-Verification (2024.findings-emnlp)
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Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu
| Challenge: | Existing tools and APIs present a challenge for generalization, despite frequent parameter updates and the daily introduction of new tools. |
| Approach: | They propose a method which distinguishes between close candidates by self-asking contrastive questions during tool selection and parameter generation. |
| Outcome: | Experiments on 4 tasks from the ToolBench benchmark show an improvement of 22% over few-shot baselines. |