训练诱导的对LLM生成内容的偏好在密集检索中的表现
Training-Induced Bias Toward LLM-Generated Content in Dense Retrieval
AI总结：
本研究发现，密集检索中的来源偏见是训练过程诱导的，而非固有属性，通过实验揭示了不同微调策略对检索结果的影响。
摘要：
密集检索是一种在开放领域自然语言处理任务中获取相关上下文或世界知识的有希望的方法，现在被广泛应用于信息检索应用中。然而，最近的报告声称存在对大型语言模型（LLM）生成文本的广泛偏好。这种偏见被称为“来源偏见”，并假设有更低的困惑度会促成这种效应。在本研究中，我们通过受控评估重新审视这一主张，以追踪这种偏好在训练阶段和数据源中的出现情况。使用平行的人类生成和LLM生成的SciFact和自然问题（NQ320K）数据集，我们比较了无监督检查点与使用领域内人类文本、领域内LLM生成文本和MS MARCO进行微调的模型。我们的结果如下：1）无监督检索器并不表现出对LLM的统一偏好。方向和程度取决于数据集。2）在所有测试设置中，使用MS MARCO进行监督微调会将排名向LLM生成文本偏移。3）领域内微调会产生特定于数据集且不一致的偏移。4）在LLM生成语料库上进行微调会诱导出明显的对LLM的偏好。最后，一个以检索器为中心的困惑度探测器，涉及将语言模型头重新连接到微调的密集检索器编码器，表明与随机相关性的一致性，从而削弱了困惑度的解释力。我们的研究证明，来源偏见是一种训练诱导的现象，而不是密集检索器的固有属性。
英文摘要：
Dense retrieval is a promising approach for acquiring relevant context or world knowledge in open-domain natural language processing tasks and is now widely used in information retrieval applications. However, recent reports claim a broad preference for text generated by large language models (LLMs). This bias is called "source bias", and it has been hypothesized that lower perplexity contributes to this effect. In this study, we revisit this claim by conducting a controlled evaluation to trace the emergence of such preferences across training stages and data sources. Using parallel human- and LLM-generated counterparts of the SciFact and Natural Questions (NQ320K) datasets, we compare unsupervised checkpoints with models fine-tuned using in-domain human text, in-domain LLM-generated text, and MS MARCO. Our results show the following: 1) Unsupervised retrievers do not exhibit a uniform pro-LLM preference. The direction and magnitude depend on the dataset. 2) Across the settings tested, supervised fine-tuning on MS MARCO consistently shifts the rankings toward LLM-generated text. 3) In-domain fine-tuning produces dataset-specific and inconsistent shifts in preference. 4) Fine-tuning on LLM-generated corpora induces a pronounced pro-LLM bias. Finally, a retriever-centric perplexity probe involving the reattachment of a language modeling head to the fine-tuned dense retriever encoder indicates agreement with relevance near chance, thereby weakening the explanatory power of perplexity. Our study demonstrates that source bias is a training-induced phenomenon rather than an inherent property of dense retrievers.
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