Summary of No Dataset Needed For Downstream Knowledge Benchmarking: Response Dispersion Inversely Correlates with Accuracy on Domain-specific Qa, by Robert L Simione Ii
No Dataset Needed for Downstream Knowledge Benchmarking: Response Dispersion Inversely Correlates with Accuracy on Domain-specific QA
by Robert L Simione II
First submitted to arxiv on: 24 Aug 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 This paper proposes a novel approach to compare large language models’ (LLMs) knowledge in specific topic domains without requiring access to their inner workings. The method, dubbed “response dispersion,” defines a metric that measures the variability of an LLM’s responses to opinion questions about a given topic domain. By calculating this dispersion value, researchers can assess the accuracy of different LLMs on relevant question-answering evaluations. The study shows that response dispersion is inversely correlated with accuracy (average Spearman rank correlation stronger than -.59). Furthermore, the authors demonstrate that comparing LLMs’ response dispersions can replace traditional QA evaluation methods in 74-89% of cases, depending on acceptable accuracy-difference tolerances. Two embedding techniques are explored: OpenAI’s API and reference sentence similarity embeddings, which can be computed locally and achieve similar results. The study also re-purposes a trivia dataset (IRC-Wiki Trivia) to create the IRC-WikiTriviaQA dataset for this research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to compare how well language models understand specific topics without looking at their “brain” or inner workings. The method, called “response dispersion,” measures how different an LLM’s answers are when asked the same question multiple times about that topic. Researchers can use this measure to figure out which language model is more accurate for a particular task. The study shows that response dispersion is linked to accuracy (the closer to 0, the better). It also finds that comparing response dispersions can replace traditional evaluation methods in most cases. Two ways of creating embeddings are explored: one from OpenAI and another method called reference sentence similarity. A trivia dataset was also used to help with this research. |
Keywords
» Artificial intelligence » Embedding » Language model » Question answering
