The article presents a heuristic-driven solution called Agentic Table Talk, which utilizes general-purpose Large Language Models (LLMs) to solve the problem of Table Question Answering (Tabular Q/A). The goal of this task is to answer the user query based on the provided table. Earlier approaches required whole tables to prompt LLMs. As tables can grow infinitely large, directly incorporating entire tables into prompts becomes infeasible due to the finite context window of LLMs, which can only handle a limited number of input tokens. In contrast, some approaches are entirely driven by Retrieval Augmented Generation (RAG) to ground the LLMs with K relevant records where the value of K is predefined and hard coded. The main drawback of such approaches is that they do not yield satisfactory results for Tabular Q/A, where data is structured and cellular. Additionally, the meaning of individual cells is often context-dependent and can vary substantially based on the values of neighboring cells. To address these challenges, this article proposes a novel combination of a lightweight heuristic called RAGular SubGraph Retrieval combined with the ReAct Agentic Framework powered by general-purpose LLMs. In this combination, the proposed heuristic adopts the core principles of traditional RAG while placing greater emphasis on preserving the neighborhood relationships of the selected cells. The ReAct Agent receives the user query along with the filtered tabular data as a subgraph that is assumed to be part of a larger, unknown graph. The output from the heuristic guides the subsequent ReAct Agent with a focused input context that consists of only relevant cells and their neighborhood, based on the given input query. The primary objective of the ReAct Agent is to use explicit reasoning to identify the facts necessary to answer the provided user query. This may involve reasoning over the known subgraph or taking actions (such as re-triggering the heuristic with a slightly modified query or finding the closest K-Adjacent neighbors of a relevant cell) to explore new, unexplored cells based on the structure of the already known regions. Both components work together iteratively to discover relevant facts for generating an authentic answer to the user query based on the provided reference table, until the process successfully locates all the necessary information or sufficient confidence in the query’s un-answerability is achieved. The proposed approach is evaluated using general-purpose LLMs of varying sizes on benchmarked AIT-QA ( https://github.com/IBM/AITQA ) and HiTab ( https://github.com/microsoft/HiTab ) datasets, along with a Synthetic Dataset (SD) ( https://github.com/FarhanDhanani/AgenticTableTalk/tree/main/dataset/synthetic ) featuring large tables. The evaluation examines how heuristic-driven input contexts affect the reasoning abilities of LLMs of varying sizes in managing complex tables and compares these results to existing methodologies. The codebase is publicly available on Git (Agentic Table Talk Approach Available At GIT: https://github.com/FarhanDhanani/AgenticTableTalk ) repository, allowing all experiments to be easily re-executed for reproducing the results.
Dhanani et al. (Tue,) studied this question.