6 papers
Which Knowledge Should LLMs Trust? Uncovering a Generalizable Steering Direction Across Knowledge Conflicts
Yibin Lou, Yi Hu, Jianhui Chen +3
Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms governing this selection rem…
From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution
Yuzhang Luo, Chenpeng Wang, Jianhui Chen +1
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they ar…
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores
Qiyao Yan, Chenpeng Wang, Liangming Pan
When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of reasoning with a late-stage…
Where Steering Signals Come From: Activation Source Selection in Activation Steering
Jiaran Ye, Lingxu Ran, Zijun Yao +5
Activation steering controls language models by adding vectors or features to hidden states at inference time, but the upstream source of these steering signals is often treated as…
Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Zhuo Zuo, Li Yue, Wenhao Zheng +2
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: st…
HOLogicBench: Evaluating Higher-Order Logical Reasoning in LLMs
Yucheng Wu, Jundong Xu, Mingzhen Ju +4
Existing LLM logical-reasoning benchmarks largely focus on object-level inference under fixed predicates and rules, leaving higher-order reasoning over rules and policies underexpl…