Summary of Mt-bench-101: a Fine-grained Benchmark For Evaluating Large Language Models in Multi-turn Dialogues, by Ge Bai et al.
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
by Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He, Jiaheng Liu, Zhanhui Zhou, Zhuoran Lin, Wenbo Su, Tiezheng Ge, Bo Zheng, Wanli Ouyang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 introduction of Large Language Models (LLMs) has significantly improved dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge due to previous benchmarks focusing on single-turn dialogues or providing coarse-grained and incomplete assessments of multi-turn dialogues. To address this issue, researchers introduce MT-Bench-101, a benchmark specifically designed to evaluate the fine-grained abilities of LLMs in multi-turn dialogues. The benchmark consists of 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks, which are categorized into a three-tier hierarchical ability taxonomy. The authors then evaluate 21 popular LLMs based on MT-Bench-101, analyzing their performance from both ability and task perspectives. The study finds that neither utilizing common alignment techniques nor chat-specific designs has led to obvious enhancements in the multi-turn abilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to test how well large language models can understand and respond to long conversations. These models are used in things like chatbots and virtual assistants, but until now there hasn’t been a good way to measure their ability to have multi-turn dialogues (conversations that go back and forth). The researchers created a special set of tests called MT-Bench-101 that can be used to see how well different language models do on these kinds of conversations. They found that some models are better than others, but none of them have really shown much improvement in this area. |
Keywords
» Artificial intelligence » Alignment
