Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?
This paper was accepted at the Workshop on Reliable and Responsible Foundation Models (RRFMs) Workshop at ICML 2025. Uncertainty quantification plays a pivotal role when bringing large language models (LLMs) to end-users. Its primary goal is that an LLM should indicate when it is unsure about an answer it gives. While this has been revealed with numerical certainty scores in the past, we propose to use the rich output space of LLMs, the space of all possible strings, to give a string that describes the uncertainty. In particular, we seek a string that describes the distribution of LLM answers…
The common approach to communicate a large language model’s (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user…
This paper was accepted to the ACL 2025 main conference as an oral presentation. This paper was accepted at the Scalable Continual Learning for Lifelong Foundation Models (SCLLFM) Workshop at NeurIPS 2024. Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update…
This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative…