Papers by Benjamin Negrevergne

1 papers
Exploring Precision and Recall to assess the quality and diversity of LLMs (2024.acl-long)

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Challenge: Existing benchmarks for large language models are limited to specific tasks, but they are now widely available for a wide range of tasks.
Approach: They propose a framework for large language models such as Llama-2 and Mistral that imports precision and recall metrics from image generation to text generation.
Outcome: The proposed framework allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.

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