It's LIT! Reliability-Optimized LLMs with Inspectable Tools

TL;DR

This paper introduces LIT, a framework that enhances LLMs by enabling them to select and use external tools based on reliability and troubleshootability, improving trustworthiness in high-stakes tasks.

Contribution

The paper presents a novel framework called LIT that guides LLMs to choose the most reliable and easy-to-troubleshoot tools for problem-solving, supported by a new benchmark dataset.

Findings

LLMs using LIT achieve higher reliability in solutions.

LIT improves troubleshooting and trustworthiness of LLM outputs.

Framework maintains task performance while enhancing reliability.

Abstract

Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world tasks. However, LLMs often follow an opaque reasoning process, which limits their usefulness in high-stakes domains where solutions need to be trustworthy to end users. LLMs can choose solutions that are unreliable and difficult to troubleshoot, even if better options are available. We address this issue by forcing LLMs to use external -- more reliable -- tools to solve problems when possible. We present a framework built on the tool-calling capabilities of existing LLMs to enable them to select the most reliable and easy-to-troubleshoot solution path, which may involve multiple sequential tool calls. We refer to this framework as LIT (LLMs with Inspectable Tools). In order to support LIT, we introduce a new and challenging benchmark dataset of 1,300 questions and a customizable set of reliability cost functions associated with a collection of specialized tools. These cost functions summarize how reliable each tool is and how easy it is to troubleshoot. For instance, a calculator is reliable across domains, whereas a linear prediction model is not reliable if there is distribution shift, but it is easy to troubleshoot. A tool that constructs a random forest is neither reliable nor easy to troubleshoot. These tools interact with the Harvard USPTO Patent Dataset and a new dataset of NeurIPS 2023 papers to solve mathematical, coding, and modeling problems of varying difficulty levels. We demonstrate that LLMs can achieve more reliable and informed problem-solving while maintaining task performance using our framework.