arXiv AI

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607. 19847v1 Announce Type: cross Abstract: Predicting missing cell values in tabular data is a fundamental problem in data cleaning.

arXiv AI

Jul 22

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.

By Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng

llms benchmarks

arXiv AI

Jul 28

Learning When to Reason for Text-to-SQL via SFT and DPO

arXiv:2607. 22622v1 Announce Type: cross Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead.

By Soohyuk Jang, Jiheum Yeom, Nohil Park, Sang Hun Kim, Yoonyoung Choi, Kiwook Bae, Sungroh Yoon

reinforcement-learning fine-tuning benchmarks data-engineering

arXiv AI

Jun 26

\textsc{DiARC}: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models

arXiv:2606. 26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~\citealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and predicting output grids.

By Yuxuan Yang, Feiyang Li, Yile Wang

llms fine-tuning benchmarks safety

arXiv AI

Jul 28

Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering

arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.

By Guixin Su, Qiankun Pi, Mayi Xu, Wenli Li, Ming Zhong, Yuanyuan Zhu, Jiawei Jiang, Tieyun Qian

llms nlp efficiency

arXiv Machine Learning

Jul 21

PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning

arXiv:2607. 18199v1 Announce Type: cross Abstract: Not all training samples contribute equally to large language model fine-tuning.

By Hang Zhang, Warren J. Gross

llms fine-tuning benchmarks data-engineering

arXiv AI

Jul 2

Predicting LLM Reasoning Performance with Small Proxy Model

arXiv:2509. 21013v4 Announce Type: replace-cross Abstract: Given the prohibitive cost of pre-training large language models, it is essential to leverage smaller proxy models to optimize datasets before scaling up.

By Woosung Koh, Juyoung Suk, Sungjun Han, Se-Young Yun, Jamin Shin

llms benchmarks safety

arXiv Machine Learning

6d ago

AutoGrable: What Is a Good Graph for a Table?

arXiv:2608. 11431v1 Announce Type: new Abstract: Graph learning presupposes a graph, and tables and relational databases do not come with one.

By Tamara Cucumides, Floris Geerts

safety

arXiv AI

Jun 29

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models

arXiv:2606. 26530v2 Announce Type: replace-cross Abstract: The Abstraction and Reasoning Corpus (ARC) contains tasks that require summarizing patterns from limited grid samples and predicting output grids.

By Yuxuan Yang, Feiyang Li, Yile Wang

llms fine-tuning benchmarks safety

arXiv AI

Jun 3

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases

arXiv:2606. 03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure.

By Phillip Jiang

llms

arXiv AI

Jun 4

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.

By Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen

reinforcement-learning

arXiv Machine Learning

Aug 7

SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models

arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.

By Yi He, Zhengkang Guan, Anpeng Wu, Peng Cui, Fei Wu, Kun Kuang

agents fine-tuning

arXiv Machine Learning

Jul 16

Algorithmic Recourse of In-Context Learning for Tabular Data

arXiv:2605. 31272v2 Announce Type: replace Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected individuals.

By Wenshuo Dong, Jiaming Zhang, Shaopeng Fu, Hongbin Lin, Di Wang, Lijie Hu

llms multimodal

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