Summary of Constructing Domain-specific Evaluation Sets For Llm-as-a-judge, by Ravi Raju et al.
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge
by Ravi Raju, Swayambhoo Jain, Bo Li, Jonathan Li, Urmish Thakker
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper introduces a novel data pipeline to create diverse, domain-specific evaluation sets for Large Language Models (LLMs) in various applications, including law, medicine, and multilingual contexts. The authors address limitations in existing frameworks by leveraging manual curation, semi-supervised learning, and stratified sampling to ensure balanced representation across domains and languages. The resulting evaluation set demonstrates high separability and agreement with top-ranked models, outperforming current benchmarks like Alpaca-Eval 2.0 LC and Arena-Hard v0.1. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well language models work in different areas, such as law and medicine. The authors want to make sure these tests are fair and show how good the models are at making decisions. They use a mix of human helpers and computers to pick the right examples for testing. This helps them create a big set of test questions that shows how well different language models work in many areas. |
Keywords
* Artificial intelligence * Semi supervised
