Summary of Is Crowdsourcing Breaking Your Bank? Cost-effective Fine-tuning Of Pre-trained Language Models with Proximal Policy Optimization, by Shuo Yang and Gjergji Kasneci
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization
by Shuo Yang, Gjergji Kasneci
First submitted to arxiv on: 28 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 This paper proposes a novel approach to fine-tuning language models using reinforcement learning from human feedback, eliminating the need for manual ranking. By employing probabilistic sampling, TextRank, and ISODATA algorithms, the method encourages language models to generate diverse responses and rank them based on semantics. The proposed reward model is used to learn the rank and optimize the generative policy. Experimental results demonstrate that the trained models outperform baselines in terms of BLEU, GLEU, and METEOR scores, with remarkable consistency with human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers smarter by teaching them to generate better responses to questions and prompts. Instead of asking humans to rate these responses, it uses a new way to rank them based on how similar they are in meaning. This makes the process faster and cheaper. The results show that this approach is very effective and produces responses that are almost as good as those rated by humans. |
Keywords
» Artificial intelligence » Bleu » Fine tuning » Reinforcement learning from human feedback » Semantics
