让大模型输出不确定性的集合，提升可信度并识别幻觉。
Random-Set Large Language Models
- 用随机集合代替概率分布，建模生成过程中的认知不确定性
- 在CoQA和OBQA上比标准模型更准确，且能检测幻觉
- 通过聚类压缩候选词集，兼顾效率与可扩展性
大语言模型虽能生成高质量文本，但其可信度存疑。本文提出随机集大语言模型（RSLLM），不再输出传统概率向量，而是预测有限的随机集合（信念函数）以表征令牌空间的不确定性。为实现高效计算，采用分层聚类方法提取少量‘焦点’令牌子集进行信念预测，避免遍历所有组合，兼顾可扩展性与有效性。RSLLM通过训练数据的规模与多样性，反映在预测信念函数对应的信用集大小中，从而编码认知不确定性。在CoQA与OBQA数据集上，使用Llama2-7b、Mistral-7b及Phi-2模型进行评估，结果表明该方法在答案正确性上优于标准模型，并具备估计第二层不确定性的潜力，同时可识别自身幻觉行为。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are known to produce very high-quality tests and responses to our queries. But how much can we trust this generated text? In this paper, we study the problem of uncertainty quantification in LLMs. We propose a novel Random-Set Large Language Model (RSLLM) approach which predicts finite random sets (belief functions) over the token space, rather than probability vectors as in classical LLMs. In order to allow so efficiently, we also present a methodology based on hierarchical clustering to extract and use a budget of "focal" subsets of tokens upon which the belief prediction is defined, rather than using all possible collections of tokens, making the method scalable yet effective. RS-LLMs encode the epistemic uncertainty induced in their generation process by the size and diversity of its training set via the size of the credal sets associated with the predicted belief functions. The proposed approach is evaluated on CoQA and OBQA datasets using Llama2-7b, Mistral-7b and Phi-2 models and is shown to outperform the standard model in both datasets in terms of correctness of answer while also showing potential in estimating the second level uncertainty in its predictions and providing the capability to detect when its hallucinating.
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