  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.
