Summary of Creative Beam Search: Llm-as-a-judge For Improving Response Generation, by Giorgio Franceschelli and Mirco Musolesi
Creative Beam Search: LLM-as-a-Judge For Improving Response Generation
by Giorgio Franceschelli, Mirco Musolesi
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); 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 This paper proposes Creative Beam Search, a method for generating responses using Large Language Models (LLMs). Unlike human creativity, machine generation lacks intentionality and an underlying creative process. The approach combines Diverse Beam Search and LLM-as-a-Judge to perform response generation and validation. Experimental results show that Creative Beam Search outperforms standard sampling techniques in terms of quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can be more creative like humans. Right now, machine creativity is very different from ours because it doesn’t have a clear purpose or process. The authors suggest a new way to generate responses using large language models that combines two ideas: searching for diverse options and having the model judge what’s good or bad. By doing this, the authors show that their method can produce better results than usual methods. |
