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This work identifies a factual-salient layer span within LLMs whose derived signal is selectively elevated for factual tokens and exhibits anomalous spikes at hallucination-prone steps, and proposes DescaPE, a decoding framework that leverages internal model signals to suppress hallucination-prone trajectories at infer...
Experiments on three LRMs show that SaLT-DPO consistently reduces unsafe rates for both reasoning and answer segments while mitigating degradation in benign compliance and preserving general reasoning performance.
This work proposes a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking and demonstrates consistent improvements in retrieval quality and downstream QA performance over baselines.
The introduction of CRiT-QA (Counterfactual Reasoning with Traps), a dataset explicitly designed to address both limitations of large language models' multi-hop reasoning, and provides a foundation for developing more reliable, evidence-grounded LLMs.
IterCOMP is proposed, a unified, training-free prompt compression framework that incorporates multi-hop reasoning within an iterative compression loop that achieves substantial improvements in Exact Match and F1 scores while reducing the token budget.
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