In this work, we present the first study to explore inference-time scaling on table reasoning tasks.We develop and evaluate two post-training strategies to enable inferencetime scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR).For distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1, which we use to fine-tune LLMs into the Table-R1-SFT model.For RLVR, we propose taskspecific verifiable reward functions and apply the GRPO algorithm to obtain the Table-R1-Zero model.We evaluate our Table-R1series models across diverse table reasoning tasks, including short-form QA, fact verification, and free-form QA.Notably, the Table-R1-Zero model matches or exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a 7B-parameter LLM.It also demonstrates strong generalization to out-of-domain datasets.Extensive ablation and qualitative analyses reveal the benefits of instruction tuning, model architecture choices, and cross-task generalization, as well as emergence of essential table reasoning skills during RL training.Model huggingface.co/Table-R1Code github.com/Table-R1

Long-form Table Question Answering (LFTQA) requires systems to generate paragraph long and complex answers to questions over tabular data.While Large language models based systems have made significant progress, it often hallucinates, especially when the task involves complex reasoning over tables.To tackle this issue, we propose a new LLM-based framework, TAPERA, for LFTQA tasks.Our framework uses a modular approach that decomposes the whole process into three sub-modules: 1) QA-based Content Planner that iteratively decomposes the input question into sub-questions; 2) Execution-based Table Reasoner that produces executable Python program for each sub-question; and 3) Answer Generator that generates long-form answer grounded on the program output.Human evaluation results on the FETAQA and QTSUMM datasets indicate that our framework significantly improves strong baselines on both accuracy and truthfulness, as our modular framework is better at table reasoning, and the long-form answer is always consistent with the program output.Our modular design further provides transparency as users are able to interact with our framework by manually changing the content plans.https://github.com/yilunzhao/TaPERAWithin the Oil and Gas industry, Sinopec Group earns the highest profit -$6,205 million.However, compared to the most profitable company overall, Apple, the profit earned by Sinopec Group is much lower.In fact, Apple earns $51,306 million more profit than Sinopec Group.

Yilun Zhao, Yitao Long, Hongjun Liu, Ryo Kamoi, Linyong Nan, Lyuhao Chen, Yixin Liu, Xiangru Tang, Rui Zhang, Arman Cohan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

In the era of data-driven decision-making, Long-Form Table Question Answering (LFTQA) is essential for integrating structured data with complex reasoning.Despite recent advancements in Large Language Models (LLMs) for LFTQA, evaluating their effectiveness remains a significant challenge.We introduce LFTQA-Eval, a meta-evaluation dataset comprising 2,988 human-annotated examples, to rigorously assess the efficacy of current automated metrics in assessing LLM-based LFTQA systems, with a focus on faithfulness and comprehensiveness.Our findings reveal that existing automatic metrics poorly correlate with human judgments and fail to consistently differentiate between factually accurate responses and those that are coherent but factually incorrect.Additionally, our in-depth examination of the limitations associated with automated evaluation methods provides essential insights for the improvement of LFTQA automated evaluation.