Summary of Can Llms Speak For Diverse People? Tuning Llms Via Debate to Generate Controllable Controversial Statements, by Ming Li et al.
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
by Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou
First submitted to arxiv on: 16 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 This paper proposes an innovative approach to improving the controllability of Large Language Models (LLMs) in generating statements that support diverse or even controversial perspectives. The authors demonstrate that multi-round debates between two LLMs with opposite stances can produce higher-quality and more salient statements, serving as valuable training data for fine-tuning LLMs. Building upon this insight, the paper develops a novel pipeline, DEBATUNE, which finetunes LLMs to generate statements obtained via debate. The authors curate a large dataset of debate topics, covering 710 controversial topics and corresponding arguments, to evaluate the effectiveness of DEBATUNE. Results show that LLMs can significantly improve their capability of generating diverse perspectives using this approach, with controllability generalizing to unseen topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make language models more inclusive by letting them speak for different people and generate statements that support their unique views. Right now, these models often produce neutral or biased statements instead. The authors found that having two language models argue back and forth can help create better and more important training data to improve the models’ ability to generate diverse perspectives. They also developed a new way to fine-tune the models using this debate approach. By testing their method on a large dataset of controversial topics, they showed that it works well and can even be applied to new topics. |
Keywords
* Artificial intelligence * Fine tuning
