EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

TL;DR

EvalLM is an interactive system that helps developers refine large language model prompts by evaluating multiple outputs against user-defined criteria, reducing effort and improving prompt quality.

Contribution

It introduces an LLM-based evaluator for prompt refinement, enabling natural language criteria specification and more efficient, diverse prompt evaluation.

Findings

Participants examined twice as many outputs with EvalLM.

EvalLM reduced the number of revisions by 59%.

The system facilitated more diverse criteria development.

Abstract

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system's LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator's feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts.