arXiv AI July 24, 2026 By Huei-Chung Hu, Hsin-Tai Wu, Koyo Kobayashi

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

arXiv:2607. 20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is answerable, but whether the computed answer is statistically meaningful.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 9

What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

arXiv:2607. 06799v1 Announce Type: cross Abstract: Evaluating uncertainty in AI-generated SQL queries requires estimating whether a query is correct, where correct means it executes to the same result as a human-written reference.

By Robert Richardson

llms fine-tuning benchmarks data-engineering

arXiv AI

Aug 13

DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation

arXiv:2608. 11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.

By Anik Pramanik, Murat Kantarcioglu, Vincent Oria, Shantanu Sharma

llms fine-tuning benchmarks safety data-engineering

arXiv Machine Learning

Aug 12

Post-Calibration Reliability Reranking of Relevance Decisions via Label-wise Monotone Projection

arXiv:2608. 10406v1 Announce Type: cross Abstract: Web search, product search, and question-answering retrieval systems often assign a relevance label and confidence score to each query-candidate pair.

By Inwoo Tae, Yongjae Lee

rag

arXiv Computation and Language

Sep 21

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

arXiv:2609.20842v1 Announce Type: new Abstract: Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-t...

By Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang

llms reinforcement-learning fine-tuning data-engineering

arXiv AI

Jul 17

Harnessing LLMs for Reliable Academic Supervision: A Comparative Study

arXiv:2607. 14707v1 Announce Type: cross Abstract: Large language models routinely produce fluent answers to single-shot prompts, yet deploying them as reliable components of a domain decision system is substantially harder.

By Akash Raj

llms benchmarks

arXiv AI

Jul 20

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data

arXiv:2607. 16122v1 Announce Type: new Abstract: Evaluations should do more than measure a models current performance.

By Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru, MohammadHossein Rezaei, Aakash Sabharwal, Yunzhong He

llms fine-tuning benchmarks

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Related stories

What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation

Post-Calibration Reliability Reranking of Relevance Decisions via Label-wise Monotone Projection

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

Harnessing LLMs for Reliable Academic Supervision: A Comparative Study

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data