Summary of Pplqa: An Unsupervised Information-theoretic Quality Metric For Comparing Generative Large Language Models, by Gerald Friedland et al.
PPLqa: An Unsupervised Information-Theoretic Quality Metric for Comparing Generative Large Language Models
by Gerald Friedland, Xin Huang, Yueying Cui, Vishaal Kapoor, Ashish Khetan, Sanjiv Das
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed paper introduces PPLqa, a novel metric to evaluate the quality of responses generated by Large Language Models (LLMs) without relying on annotated data or human supervision. This metric assesses the quality of responses based on coherence, fluency, relevance, and consistency, effectively ranking LLMs for their performance in generating high-quality responses. PPLqa is shown to be comparable to existing metrics and particularly effective when evaluating long-form question-answering models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PPLqa is a new way to measure how well language models answer questions without needing special training data or human help. It looks at how good the answers are, considering things like how clear they are, how relevant they are to the question, and how consistent they are with what the model has said before. This helps us pick the best language model for a task. PPLqa works just as well as other ways to measure language models, and it’s especially good at looking at longer answers. |
Keywords
» Artificial intelligence » Language model » Question answering
