Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
This addresses reliability risks in multi-turn or agentic LLM applications, but it is incremental as it builds on existing uncertainty methods.
The paper tackled the problem of LLMs generating incorrect but fluent content (confabulation) by investigating whether they can detect their unreliable responses, finding that correct in-context information improves accuracy and confidence while misleading context leads to confidently incorrect outputs, with their method improving detection across multiple LLMs.
Large Language Models (LLMs) are prone to generating fluent but incorrect content, known as confabulation, which poses increasing risks in multi-turn or agentic applications where outputs may be reused as context. In this work, we investigate how in-context information influences model behavior and whether LLMs can identify their unreliable responses. We propose a reliability estimation that leverages token-level uncertainty to guide the aggregation of internal model representations. Specifically, we compute aleatoric and epistemic uncertainty from output logits to identify salient tokens and aggregate their hidden states into compact representations for response-level reliability prediction. Through controlled experiments on open QA benchmarks, we find that correct in-context information improves both answer accuracy and model confidence, while misleading context often induces confidently incorrect responses, revealing a misalignment between uncertainty and correctness. Our probing-based method captures these shifts in model behavior and improves the detection of unreliable outputs across multiple open-source LLMs. These results underscore the limitations of direct uncertainty signals and highlight the potential of uncertainty-guided probing for reliability-aware generation.