Summary of Reference-guided Verdict: Llms-as-judges in Automatic Evaluation Of Free-form Text, by Sher Badshah et al.
Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form Text
by Sher Badshah, Hassan Sajjad
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper proposes a novel evaluation method for Large Language Models (LLMs) on open-ended tasks. The conventional metrics BLEU and ROUGE are insufficient to capture the subtleties of generative outputs, leading to an increased need for robust evaluation methods. The reference-guided verdict method automates the process by leveraging multiple LLMs-as-judges. Experiments on three question-answering tasks demonstrate that combining multiple judges improves reliability and accuracy, particularly in complex tasks where a single model might struggle. The findings show a strong correlation with human evaluations, making this method a viable alternative to traditional metrics and human judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to test chatbots like Siri or Alexa. Right now, we use simple tests that aren’t very good at capturing how well the chatbot really understands what you’re saying. The authors of this paper came up with a new way to test these chatbots by having multiple chatbots look at the same conversation and agree on whether it’s correct or not. They tested their method on three different tasks and found that it worked really well, especially for tricky questions where one chatbot might get it wrong. |
Keywords
» Artificial intelligence » Bleu » Question answering » Rouge
