通过叙事检测语言模型中的模式坍缩
Detecting Mode Collapse in Language Models via Narration
AI总结：
本研究通过分析OpenAI GPT-3系列模型生成的4,374个故事，发现对齐训练导致语言模型出现模式坍缩，使其丧失模拟多样作者视角的能力，对社会学模拟应用具有警示意义。
摘要：
没有两位作者的写作风格完全相同。从词汇到修辞手法，书面叙事中体现的个人风格暗示着特定的作者——文学理论家称之为隐含作者或虚拟作者，区别于文本的真实作者或叙述者。早期的大语言模型在未经筛选的训练集上训练，这些训练集来自各种不一致的来源，导致模型产生不连贯的人格，这对对话任务是有问题的，但对于从多视角采样文学作品却很有用。近年来对齐研究的成功使研究人员能够通过指令微调和基于人类反馈的强化学习（RLHF）为语言模型施加主观一致的人格，但对齐后的模型是否保留建模任意虚拟作者的能力却很少受到审视。通过研究从三个OpenAI语言模型中采样的4,374个故事，我们表明GPT-3的连续版本遭受日益严重的“模式坍缩”，即在对齐过程中对模型的过拟合限制了其对作者身份的泛化能力：遭受模式坍缩的模型无法承担多种视角。我们的方法和结果对于寻求在社会学模拟中使用语言模型的研究人员具有重要意义。
英文摘要：
No two authors write alike. Personal flourishes invoked in written narratives, from lexicon to rhetorical devices, imply a particular author--what literary theorists label the implied or virtual author; distinct from the real author or narrator of a text. Early large language models trained on unfiltered training sets drawn from a variety of discordant sources yielded incoherent personalities, problematic for conversational tasks but proving useful for sampling literature from multiple perspectives. Successes in alignment research in recent years have allowed researchers to impose subjectively consistent personae on language models via instruction tuning and reinforcement learning from human feedback (RLHF), but whether aligned models retain the ability to model an arbitrary virtual author has received little scrutiny. By studying 4,374 stories sampled from three OpenAI language models, we show successive versions of GPT-3 suffer from increasing degrees of "mode collapse" whereby overfitting the model during alignment constrains it from generalizing over authorship: models suffering from mode collapse become unable to assume a multiplicity of perspectives. Our method and results are significant for researchers seeking to employ language models in sociological simulations.
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。
