InternalInspector $I^2$: Robust Confidence Estimation in LLMs through Internal States
This addresses the issue of hallucinations in LLMs for users needing reliable outputs, though it is incremental as it builds on existing internal state methods.
The research tackled the problem of unreliable confidence estimation in Large Language Models (LLMs) by introducing InternalInspector, a framework that uses contrastive learning on internal states to improve alignment between confidence scores and prediction correctness, achieving higher accuracy and lower calibration error across various tasks.
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations. Addressing this challenge, our research introduces InternalInspector, a novel framework designed to enhance confidence estimation in LLMs by leveraging contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers. Unlike existing methods that primarily focus on the final activation state, InternalInspector conducts a comprehensive analysis across all internal states of every layer to accurately identify both correct and incorrect prediction processes. By benchmarking InternalInspector against existing confidence estimation methods across various natural language understanding and generation tasks, including factual question answering, commonsense reasoning, and reading comprehension, InternalInspector achieves significantly higher accuracy in aligning the estimated confidence scores with the correctness of the LLM's predictions and lower calibration error. Furthermore, InternalInspector excels at HaluEval, a hallucination detection benchmark, outperforming other internal-based confidence estimation methods in this task.