使用语言模型写作会降低内容多样性吗？
Does Writing with Language Models Reduce Content Diversity?
AI总结：
本文通过受控实验发现，使用经反馈调优的InstructGPT辅助写作会显著降低内容多样性，且该效应主要源于模型贡献的文本更同质，揭示了模型对齐人类反馈可能以牺牲多样性为代价。
摘要：
大型语言模型（LLMs）引发了模型辅助协作写作的热潮。由于不同用户会采纳同一模型的建议，所生成内容存在多样性下降的风险，可能限制公共话语中的多元视角。在本工作中，我们通过一项受控实验来衡量共同写作对多样性的影响：用户在三种设置下撰写议论文——使用基础LLM（GPT3）、使用经反馈调优的LLM（InstructGPT）以及不使用模型辅助写作。我们开发了一套多样性度量指标，发现使用InstructGPT写作（但使用GPT3时并非如此）会导致多样性出现统计上显著的下降。具体而言，它提高了不同作者文本之间的相似度，并降低了整体词汇和内容多样性。我们还发现，这一效应主要可归因于InstructGPT为共同写作的论文贡献了多样性较低的文本。相比之下，用户贡献的文本不受模型协作的影响。这表明，通过使模型适应人类反馈而带来的生成质量近期提升，可能以内容更加同质化、多样性更低为代价。
英文摘要：
Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
