Summary of Fennec: Fine-grained Language Model Evaluation and Correction Extended Through Branching and Bridging, by Xiaobo Liang et al.
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and Bridging
by Xiaobo Liang, Haoke Zhang, Helan hu, Juntao Li, Jun Xu, Min Zhang
First submitted to arxiv on: 20 May 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 abstract presents a novel approach to alleviating the reliance on human evaluation in large language models by employing open-source models as evaluators. The authors introduce the Fennec framework, which consists of branching and bridging operations to fine-grain evaluate and correct extended tasks. The framework is capable of outperforming larger-scale evaluation models across various benchmarks in terms of Agreement and Consistency, approaching the capabilities of GPT-4. The authors demonstrate the refinement capabilities induced by the evaluation model, leading to an improvement of 1-2 points on the MT-Bench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are helping computers understand human intent better, but it takes a lot of time and effort for humans to check if they’re doing a good job. To make things easier, scientists created a new way to use big AI models as judges instead. This approach is called Fennec, and it’s designed to help evaluate how well other AI models are doing their jobs. By breaking down tasks into smaller parts and combining data from different sources, Fennec can accurately judge the quality of AI responses. |
Keywords
* Artificial intelligence * Gpt
