Summary of Grade Score: Quantifying Llm Performance in Option Selection, by Dmitri Iourovitski
Grade Score: Quantifying LLM Performance in Option Selection
by Dmitri Iourovitski
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 study introduces the “Grade Score”, a novel metric to evaluate the consistency and fairness of Large Language Models (LLMs) when used as multiple-choice judges. The Grade Score combines Entropy, which measures order bias, and Mode Frequency, which assesses choice stability. The authors explore techniques like prompt engineering and option sampling strategies to optimize the Grade Score, demonstrating their effectiveness in enhancing LLMs’ performance. Results show varying performances among LLMs with respect to prompts, highlighting the positive impact of including irrelevant options. The study identifies an emergent behavior in instruction-following models, adapting to instructions targeting specific biases. The Grade Score facilitates comparisons between LLMs and encourages ongoing research towards optimizing their decision-making processes, with potential implications for improving reliability and fairness in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study creates a new way to measure how well Large Language Models (LLMs) do when used as multiple-choice judges. They designed the “Grade Score” to see if LLMs are consistent and fair. The score combines two things: entropy, which shows order bias, and mode frequency, which shows choice stability. To make it work better, they tried different techniques like changing prompts and adding extra options. This helped some LLMs do better. They also found that some models can change how they answer based on the instructions given. The new score helps compare LLMs and encourages more research to make them better. |
Keywords
» Artificial intelligence » Prompt
