用训练数据提炼推理条件，提升大模型表格理解能力
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
- 从训练数据中提取错误原因，生成可复用的推理条件
- 在推理时检索相关条件，使回答准确率显著提升
- 无需微调，适合快速部署到新表格任务
自动化表格理解和推理对数据科学家至关重要。近年来，大语言模型（LLMs）在表格推理任务中应用日益广泛。以往工作主要分为两类：一是使用标注数据微调模型，虽具任务特异性但泛化能力差；二是无需训练的思维链（CoT）提示方法，虽通用性强但未充分利用训练数据。本文提出一种新型基于提示的推理方法——LRTab，通过从训练数据中检索相关知识，融合两者优势。首先，对训练数据生成CoT响应；对错误答案，提示模型预测导致错误的‘提示条件’，从中学习经验。利用验证集评估这些条件的有效性。推理时，根据当前问题检索最相关的提示条件作为额外上下文。在WikiTQ和Tabfact数据集上的全面实验表明，LRTab具有可解释性、成本低，且优于现有基线。
原文摘要 · Abstract (English)
Automated tabular understanding and reasoning are essential tasks for data scientists. Recently, Large language models (LLMs) have become increasingly prevalent in tabular reasoning tasks. Previous work focuses on (1) finetuning LLMs using labeled data or (2) Training-free prompting LLM agents using chain-of-thought (CoT). Finetuning offers dataset-specific learning at the cost of generalizability. Training-free prompting is highly generalizable but does not take full advantage of training data. In this paper, we propose a novel prompting-based reasoning approach, Learn then Retrieve: LRTab, which integrates the benefits of both by retrieving relevant information learned from training data. We first use prompting to obtain CoT responses over the training data. For incorrect CoTs, we prompt the LLM to predict Prompt Conditions to avoid the error, learning insights from the data. We validate the effectiveness of Prompt Conditions using validation data. Finally, at inference time, we retrieve the most relevant Prompt Conditions for additional context for table understanding. We provide comprehensive experiments on WikiTQ and Tabfact, showing that LRTab is interpretable, cost-efficient, and can outperform previous baselines in tabular reasoning.
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