Tag
This paper analyzes hallucination detection in LLMs, proposing a max-pooling approach that improves efficiency by eliminating costly semantic consistency computations while maintaining competitive performance.
This paper proposes a novel framework combining Large Language Models with Multiple-Instance Learning to detect cognitive distortions in mental health texts by decomposing utterances into Emotion, Logic, and Behavior components and using multi-view gated attention for classification. The approach demonstrates improved performance on Korean and English datasets, particularly for distortions with high interpretive ambiguity.