消除大语言模型幻觉需要重新思考泛化
Banishing LLM Hallucinations Requires Rethinking Generalization
AI总结：
该研究挑战了LLM幻觉源于创造性与事实性平衡的传统观点，通过实验和理论证明训练损失阈值是幻觉成因，并据此设计出搭载动态检索记忆专家混合体的Lamini-1模型以消除幻觉。
摘要：
尽管大语言模型（LLM）具备强大的聊天、编程与推理能力，却频繁出现幻觉。传统观点认为，幻觉是创造性与事实性之间平衡的产物，通过让LLM依托外部知识源可缓解但无法根除该问题。我们通过大量系统性实验表明，这些传统方法无法解释LLM在实际应用中产生幻觉的原因。具体而言，我们发现搭载大规模记忆专家混合体（Mixture of Memory Experts，MoME）的LLM能够轻松记忆包含随机数字的大型数据集。我们通过理论构建佐证了这些实验结果：训练用于预测下一个词元的简单神经网络，当训练损失高于阈值时会产生幻觉——这在互联网规模数据上训练的实际场景中十分常见。我们通过与传统的幻觉缓解检索方法对比，阐释了研究发现。基于这些发现，我们设计了首款消除幻觉的模型Lamini-1，它将事实存储在由数百万个记忆专家组成的大规模混合体中，可动态检索这些专家。
英文摘要：
Despite their powerful chat, coding, and reasoning abilities, Large Language Models (LLMs) frequently hallucinate. Conventional wisdom suggests that hallucinations are a consequence of a balance between creativity and factuality, which can be mitigated, but not eliminated, by grounding the LLM in external knowledge sources. Through extensive systematic experiments, we show that these traditional approaches fail to explain why LLMs hallucinate in practice. Specifically, we show that LLMs augmented with a massive Mixture of Memory Experts (MoME) can easily memorize large datasets of random numbers. We corroborate these experimental findings with a theoretical construction showing that simple neural networks trained to predict the next token hallucinate when the training loss is above a threshold as it usually does in practice when training on internet scale data. We interpret our findings by comparing against traditional retrieval methods for mitigating hallucinations. We use our findings to design a first generation model for removing hallucinations -- Lamini-1 -- that stores facts in a massive mixture of millions of memory experts that are retrieved dynamically.
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