Summary of Bridging the Gap Between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation, by Jaechang Kim and Jinmin Goh and Inseok Hwang and Jaewoong Cho and Jungseul Ok
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation
by Jaechang Kim, Jinmin Goh, Inseok Hwang, Jaewoong Cho, Jungseul Ok
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Concept-guided Chess Commentary generation (CCC) model integrates the decision-making strengths of expert models with the linguistic fluency of Large Language Models (LLMs), prioritizing concept-based explanations. The approach addresses the gap between expert models and LLMs in generating commentary for complex decision-making processes like chess. The CCC model is evaluated using GPT-based Chess Commentary Evaluation (GCC-Eval), which assesses informativeness and linguistic quality based on expert knowledge. Experimental results validate CCC’s ability to generate accurate, informative, and fluent commentary. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are super smart at making decisions, but they don’t explain why they made those decisions. Big language models can write a lot of words, but sometimes they make things up. To fix this problem, researchers created a new way to get expert models and big language models to work together. They called it Concept-guided Chess Commentary generation (CCC). This approach makes the expert model’s good decision-making skills match up with the language model’s ability to write clearly. The team also developed GPT-based Chess Commentary Evaluation (GCC-Eval) to test how well CCC does its job. In tests, CCC produced commentary that was accurate, informative, and easy to understand. |
Keywords
» Artificial intelligence » Deep learning » Gpt » Language model
