Uncertainty estimation for language models often treats reliability as a property of the answer distribution: high confidence, stable samples, or low semantic entropy are taken as signs that an answer can be trusted. Table reasoning exposes a fundamentally different failure mode. A model may consistently return the same answer while linking the wrong entity, selecting the wrong column, applying the wrong operation, or relying on incomplete or insufficient evidence. We introduce MIRAGE—Model Inference Reliability Assessment via Grounded Evidence, a structure-grounded reliability framework for selective table reasoning. MIRAGE estimates residual structural uncertainty from test-time traces, combining outcome consistency with grounding and computation signals, and produces calibrated answer-or-abstain decisions with lightweight reliability certificates. On a controlled four-family diagnostic benchmark, structural reliability signals substantially reduce selective risk compared with generic confidence, sampling, semantic-entropy, and hallucination-detection baselines.

Retrieval-Augmented Large Language Models(RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs’ answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging open-domain question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs’ data quality and retrieval precision jointly.