Summary of Justrank: Benchmarking Llm Judges For System Ranking, by Ariel Gera et al.
JuStRank: Benchmarking LLM Judges for System Ranking
by Ariel Gera, Odellia Boni, Yotam Perlitz, Roy Bar-Haim, Lilach Eden, Asaf Yehudai
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the pressing need for systematic comparisons between generative AI models and configurations. To achieve this, it leverages Large Language Model (LLM)-based judges to evaluate these models. However, validating the quality of LLM judges is crucial before employing them for system-level ranking. The authors argue that previous instance-based assessments overlook critical factors like positive or negative biases towards certain systems. To fill this gap, the study conducts a large-scale analysis of LLM judges as system rankers, comparing their judgments to human-based rankings. Additionally, it provides a fine-grained characterization of judge behavior, including decisiveness and bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different AI models to help us choose the best one for certain tasks. It uses special computers called Large Language Models (LLMs) to make these comparisons. But first, we need to check if these LLMs are good at judging other AI models. This study shows that some LLMs might be biased towards certain AI systems, which means they might not give accurate scores. The authors looked at how well the LLMs did when compared to human rankings and found that some were better than others. |
Keywords
» Artificial intelligence » Large language model
