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Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction
Yucheng Ruan, Ling Huang, Qika Lin +2
Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these models in high-stakes real-…
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
Tianzhe Zhao, Jiaoyan Chen, Shuxiu Zhang +3
Large language models (LLMs) have achieved remarkable success across a wide range of applications especially when augmented by external knowledge through retrieval-augmented genera…
From Latent Signals to Reflection Behavior: Tracing Meta-Cognitive Activation Trajectory in R1-Style LLMs
Yanrui Du, Yibo Gao, Sendong Zhao +6
R1-style LLMs have attracted growing attention for their capacity for self-reflection, yet the internal mechanisms underlying such behavior remain unclear. To bridge this gap, we a…
S3-CoT: Self-Sampled Succinct Reasoning Enables Efficient Chain-of-Thought LLMs
Yanrui Du, Sendong Zhao, Yibo Gao +9
Large language models (LLMs) equipped with chain-of-thought (CoT) achieve strong performance and offer a window into LLM behavior. However, recent evidence suggests that improvemen…
Concept than Document: Context Compression via AMR-based Conceptual Entropy
Kaize Shi, Xueyao Sun, Xiaohui Tao +3
Large Language Models (LLMs) face information overload when handling long contexts, particularly in Retrieval-Augmented Generation (RAG) where extensive supporting documents often…
Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
Yanrui Du, Fenglei Fan, Sendong Zhao +6
Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance various abilities of Large Language Models (LLMs). However, prior studies ha…