Summary of Exploring Precision and Recall to Assess the Quality and Diversity Of Llms, by Florian Le Bronnec et al.
Exploring Precision and Recall to assess the quality and diversity of LLMs
by Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre, Alexandre Allauzen
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel framework evaluates Large Language Models (LLMs) like Llama-2 and Mistral by importing Precision and Recall metrics from image generation to text generation, allowing for a nuanced assessment of generated text quality and diversity without relying on aligned corpora. The study reveals new insights into state-of-the-art language models’ performance on open-ended tasks not captured by traditional benchmarks, highlighting a trade-off between quality and diversity when fine-tuning or using human feedback. This work extends the toolkit for distribution-based NLP evaluation, offering practical capabilities and challenges that current LLMs face in generating diverse and high-quality text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to check how well language models do at creating text. It’s like comparing how good their drawing skills are! Instead of just looking at how well they match what we already know, this method looks at the quality and variety of the text they create. The results show that there’s a trade-off between making good sentences and making new ones. When these models get better or use human help, it gets even more complicated. This research helps us understand how to evaluate language models in a way that makes sense. |
Keywords
* Artificial intelligence * Fine tuning * Image generation * Llama * Nlp * Precision * Recall * Text generation
