Summary of Kieval: a Knowledge-grounded Interactive Evaluation Framework For Large Language Models, by Zhuohao Yu et al.
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
by Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang, Xing Xie, Yue Zhang, Shikun Zhang
First submitted to arxiv on: 23 Feb 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 The proposed KIEval framework addresses the limitations of automatic evaluation methods for large language models (LLMs) by introducing a novel, knowledge-grounded approach to assess model performance while resisting data contamination. The framework employs an LLM-powered “interactor” role to generate dynamic, multi-round dialogues that evaluate a model’s ability to apply domain-specific knowledge in complex conversations. Extensive experiments on seven leading LLMs across five datasets demonstrate the effectiveness and generalizability of KIEval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KIEval is a new way to test how well language models can understand and use what they’ve learned. Right now, people are worried that some tests might be fake or biased, which makes it hard to know how good these models really are. The researchers came up with a clever idea: instead of just asking the model simple questions, they have a conversation with it. They start by asking a question and then ask follow-up questions based on what the model says. This helps figure out if the model is just recalling things it learned or actually understanding what’s going on. |
