Random-Set Large Language Models

Large Language Models (LLMs) are known to produce very high-quality texts 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 (RS-LLM) 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 multiple datasets using Llama2-7b, Llama3.1-8b, Mistral-7b and Phi-2 models and compared against existing uncertainty methods, and is shown to outperform the standard model and existing uncertainty methods in both datasets in terms of correctness of answer, while also showing potential in estimating the second-level uncertainty of its predictions and empowering the ability to detect when it is hallucinating.