Large Language Models (LLMs) have been achieving competent performance on a
wide range of downstream tasks, yet existing work shows that inference on
structured data is challenging for LLMs. This is because LLMs need to either
understand long structured data or select the most relevant evidence before
inference, and both approaches are not trivial. This paper proposes a
framework, Learning to Reduce, that fine-tunes a language model with On-Policy
Learning to generate a reduced version of an input structured data. When
compared to state-of-the-art LLMs like GPT-4, Learning to Reduce not only
achieves outstanding performance in reducing the input, but shows
generalizability on different datasets. We further show that the model
fine-tuned with our framework helps LLMs better perform on table QA tasks
especially when the context is longer.

通过 On-Policy Learning 细调语言模型以生成输入结构化数据的简化版本，Learning to Reduce 框架在减少输入中取得出色表现，并在不同数据集上展现了泛化能力，对于长篇上下文的表格问答任务尤为有效。

学习减少：关于提高大型语言模型在结构化数据上的性能

Learning to Reduce: Towards Improving Performance of Large Language  Models on Structured Data

Large Language Models (LLMs) have been widely used as general-purpose AI
agents showing comparable performance on many downstream tasks. However,
existing work shows that it is challenging for LLMs to integrate structured
data (e.g. KG, tables, DBs) into their prompts; LLMs need to either understand
long text data or select the most relevant evidence prior to inference, and
both approaches are not trivial.
In this paper, we propose a framework, Learning to Reduce, that fine-tunes a
language model to generate a reduced version of an input context, given a task
description and context input. The model learns to reduce the input context
using On-Policy Reinforcement Learning and aims to improve the reasoning
performance of a fixed LLM. Experimental results illustrate that our model not
only achieves comparable accuracies in selecting the relevant evidence from an
input context, but also shows generalizability on different datasets. We
further show that our model helps improve the LLM's performance on downstream
tasks especially when the context is long.

使用 On-Policy 强化学习，我们提出了一个学习缩减的模型，旨在改进固定的大型语言模型的推理性能，在选择相关证据和处理长文本语境方面取得了良好的结果。