Summary of Livebench: a Challenging, Contamination-free Llm Benchmark, by Colin White et al.
LiveBench: A Challenging, Contamination-Free LLM Benchmark
by Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Ben Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to evaluating language models (LLMs) is presented, addressing the issue of test set contamination where test data is used to train newer models, rendering benchmarks obsolete. The proposed LiveBench aims to mitigate this by introducing a new benchmark that scores answers automatically based on objective ground-truth values and contains challenging tasks spanning various domains, including math, coding, reasoning, language, instruction following, and data analysis. This benchmark is designed to be immune to both test set contamination and biases introduced by human or LLM judging. LiveBench evaluates prominent closed-source models as well as open-source models with varying sizes, achieving below 65% accuracy for top models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are special computers that can understand and generate human-like language. Right now, there’s a problem with how we test these computer models to see which ones are the best. Sometimes, when we test one model, it gets access to information it shouldn’t have. This makes it hard to compare different models fairly. A group of researchers has created a new way to test LLMs that tries to fix this problem. They call it LiveBench. LiveBench has lots of challenging questions in different subjects like math and language. The answers are scored automatically, so we don’t need people or computers to decide what’s correct. This makes it more fair and allows us to compare different models better. |
