Large Language Models based Table Question Answering (LLMs-based TableQA) models excel in NLP field, however, they occasionally exhibit an unfaithful behavior where correct answers are derived through erroneous reasoning paths. In this condition, we propose TrustTable, a neuro-symbolic framework designed to ensure reasoning faithfulness by auditing the reasoning processes of LLMs. Unlike monolithic LLM-based auditors, TrustTable decouples the auditing operation into two orthogonal dimensions. It enforces factual grounding by executing neurally generated Pandas code against the table, and ensures logical soundness by verifying reasoning chains through a LLM-synthesized formal solver. By integrating these symbolic checks, TrustTable enables a Label-Free Audit Loop that systematically identifies and rectifies reasoning flaws without human supervision. In addition, we present the TrustTable-Bench, a diagnostic dataset containing diverse error categories that range from calculation discrepancies to schema misalignments. This benchmark allows for a rigorous quantification of reasoning limitations. Experiments demonstrate that our symbolic audit detects reasoning flaws more accurately than advanced baselines. More broadly, the TrustTable outperforms LLM judges in both majority voting with logical weighting and rejection sampling with process supervision.

Unsupervised cross-domain keyphrase generation is crucial in real-world natural language processing scenarios. However, the accuracy of up-to-date approaches is limited by the distribution shift between source and target domain, which stems from the cross-domain field. Large language models (LLMs) offer potential for the cross-domain keyphrase generation tasks due to their strong generalization abilities, facilitated by providing demonstrations relevant to the target task. Nevertheless, it is often difficult to obtain labeled samples from the target domain. To address this challenge, this paper aims to seek rational demonstrations from the source domain, thereby improving the LLMs’ ability in the unsupervised cross-domain keyphrase generation setting. Specifically, we design a novel domain-aware retrieval model on the source domain. Guided by insights from domain generalization theory, we introduce two generalization terms, one for cross-domain relevance and another for each domain consistency to better support retrieval of rational demonstrations. By the retrieved source-domain demonstrations and distance-based relevant score, the proposed approach achieves optimal accuracy. Comprehensive experiments on widely used cross-domain KG benchmarks demonstrate our approach’s state-of-the-art performance and effectiveness.