TopBench: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering

Problem
This paper addresses a gap in the existing literature on Table Question Answering (TQA) by focusing on implicitly predictive queries, which require models to infer unobserved answers based on historical data patterns rather than simply retrieving information. While large language models (LLMs) have made significant strides in TQA, they predominantly excel at direct information extraction and aggregation tasks. The authors highlight that current benchmarks do not adequately evaluate the ability of models to recognize latent intents and perform reliable predictive reasoning over extensive tabular data. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce TopBench, a benchmark designed specifically for evaluating LLMs on implicit prediction tasks within TQA. TopBench comprises 779 samples categorized into four sub-tasks: single-point prediction, decision-making, treatment effect analysis, and complex filtering. Each task requires models to generate outputs that include both reasoning text and structured tables. The evaluation framework assesses models under two workflows: text-based and agentic, allowing for a comprehensive analysis of their performance. The authors emphasize the need for models to integrate advanced reasoning capabilities to improve predictive accuracy, suggesting that intent recognition is a critical precursor to effective predictive behavior.

Results
The experiments conducted using TopBench reveal that existing models struggle significantly with intent recognition, often resorting to simple lookup strategies rather than engaging in the necessary predictive reasoning. The results indicate that models achieve suboptimal performance across the benchmark tasks, with a notable deficiency in handling complex queries that require deeper reasoning. While specific numerical results are not disclosed in the abstract, the authors imply that the performance gap is substantial, underscoring the inadequacy of current models in addressing the challenges posed by implicit prediction in TQA.

Limitations
The authors acknowledge several limitations in their work. First, the benchmark is relatively new and may not encompass the full diversity of implicit prediction tasks encountered in real-world applications. Additionally, the reliance on LLMs may introduce biases inherent to these models, which could affect generalizability. The authors do not discuss potential scalability issues related to the benchmark’s size or the computational resources required for training and evaluating models on the tasks. Furthermore, the lack of a comparative analysis with state-of-the-art models in the domain limits the contextual understanding of the results.

Why it matters
The introduction of TopBench has significant implications for future research in TQA and LLM development. By highlighting the importance of intent recognition and predictive reasoning, this work encourages the exploration of more sophisticated modeling techniques that can better handle implicit queries. The benchmark serves as a critical tool for evaluating and advancing the capabilities of LLMs in real-world applications, where predictive reasoning is essential. This research could pave the way for improved methodologies in TQA, ultimately enhancing the utility of LLMs in various domains, including healthcare, finance, and decision support systems.

Authors: An-Yang Ji, Jun-Peng Jiang, De-Chuan Zhan, Han-Jia Ye
Source: arXiv:2604.28076
URL: https://arxiv.org/abs/2604.28076v1