Recent work shows that context can both amplify and mitigate social bias in LLMs. Yet a key question remains underexplored: when context provides counter-stereotypic evidence, is that evidence taken up equitably across demographic targets? Using agency–communality bias as a testbed, we introduce a sequential evaluation framework that holds total counter-evidence fixed while varying its evidence structure, adapting the bookkeeping–subtyping contrast from social cognition. Across three instruction-tuned LLMs evaluated in English, counter-evidence is taken up in explicit group-level judgement and transfers to explicit judgement about held-out individuals. The prior-category structure of this uptake, however, does not carry over to generation: communal-prior targets lose their correction advantage, absorb evidence more slowly, and the weakest uptake repeatedly appears for Asian and Hispanic female targets within our controlled target set. The tested prompt instruction and DPO fine-tuning variants yield partial improvements at prior-category level but do not reliably repair these target-level gaps. These findings show that fairness audits for context-rich LLM systems should go beyond checking whether corrective evidence is present and test whether models use that evidence equitably in downstream generation.

A scientific claim typically begins with the formulation of a research question or hypothesis, which is a tentative statement or proposition about a phenomenon or relationship between variables. Within the realm of scientific claim verification, considerable research efforts have been dedicated to attention architectures and leveraging the text comprehension capabilities of Pre-trained Language Models (PLMs), yielding promising performances. However, these models overlook the causal structure information inherent in scientific claims, thereby failing to establish a comprehensive chain of causal inference. This paper delves into the exploration to highlight the crucial role of qualitative causal structure in characterizing and verifying scientific claims based on evidence. We organize the qualitative causal structure into a heterogeneous graph and propose a novel attention-based graph neural network model to facilitate causal reasoning across relevant causally-potent factors. Our experiments demonstrate that by solely utilizing the qualitative causal structure, the proposed model achieves comparable performance to PLM-based models. Furthermore, by incorporating semantic features, our model outperforms state-of-the-art approaches comprehensively.