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EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation

Adam Dejl, Jonathan Pearson

TL;DR

EvalSense is presented, a flexible, extensible framework for constructing domain-specific evaluation suites for LLMs that provides out-of-the-box support for a broad range of model providers and evaluation strategies, and assists users in selecting and deploying suitable evaluation methods for their specific use-cases.

Abstract

Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying effective LLM system configurations and mitigating risks associated with deploying LLMs in sensitive domains. However, traditional statistical metrics are poorly suited to open-ended generation tasks, leading to growing reliance on LLM-based evaluation methods. These methods, while often more flexible, introduce additional complexity: they depend on carefully chosen models, prompts, parameters, and evaluation strategies, making the evaluation process prone to misconfiguration and bias. In this work, we present EvalSense, a flexible, extensible framework for constructing domain-specific evaluation suites for LLMs. EvalSense provides out-of-the-box support for a broad range of model providers and evaluation strategies, and assists users in selecting and deploying suitable evaluation methods for their specific use-cases. This is achieved through two unique components: (1) an interactive guide aiding users in evaluation method selection and (2) automated meta-evaluation tools that assess the reliability of different evaluation approaches using perturbed data. We demonstrate the effectiveness of EvalSense in a case study involving the generation of clinical notes from unstructured doctor-patient dialogues, using a popular open dataset. All code, documentation, and assets associated with EvalSense are open-source and publicly available at https://github.com/nhsengland/evalsense.

EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation

TL;DR

EvalSense is presented, a flexible, extensible framework for constructing domain-specific evaluation suites for LLMs that provides out-of-the-box support for a broad range of model providers and evaluation strategies, and assists users in selecting and deploying suitable evaluation methods for their specific use-cases.

Abstract

Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying effective LLM system configurations and mitigating risks associated with deploying LLMs in sensitive domains. However, traditional statistical metrics are poorly suited to open-ended generation tasks, leading to growing reliance on LLM-based evaluation methods. These methods, while often more flexible, introduce additional complexity: they depend on carefully chosen models, prompts, parameters, and evaluation strategies, making the evaluation process prone to misconfiguration and bias. In this work, we present EvalSense, a flexible, extensible framework for constructing domain-specific evaluation suites for LLMs. EvalSense provides out-of-the-box support for a broad range of model providers and evaluation strategies, and assists users in selecting and deploying suitable evaluation methods for their specific use-cases. This is achieved through two unique components: (1) an interactive guide aiding users in evaluation method selection and (2) automated meta-evaluation tools that assess the reliability of different evaluation approaches using perturbed data. We demonstrate the effectiveness of EvalSense in a case study involving the generation of clinical notes from unstructured doctor-patient dialogues, using a popular open dataset. All code, documentation, and assets associated with EvalSense are open-source and publicly available at https://github.com/nhsengland/evalsense.
Paper Structure (22 sections, 2 figures, 7 tables)

This paper contains 22 sections, 2 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Evaluation Metrics
  4. LLM Evaluation Toolkits
  5. EvalSense Pipeline
  6. Dataset Manager
  7. Task Preprocessor
  8. LLM Generation Steps
  9. Evaluator
  10. Result Analyser
  11. Pipeline
  12. Project
  13. Evaluation Case Study
  14. Task Setup
  15. Evaluation Setup
  16. ...and 7 more sections

Figures (2)

  • Figure 1: Overview of the LLM evaluation pipeline implemented in EvalSense. After data loading and task-specific preprocessing, model outputs are generated and scored using different evaluators. Result analysers summarise outcomes across experiments, identify higher-level patterns, and support meta-evaluation.
  • Figure 2: (a) EvalSense's LLM Evaluation Guide assists users in selecting suitable evaluation methods based on task-specific risks and requirements. The final evaluation plan summary highlights any risks and requirements not fully covered by the selected methods. The guide is available at https://nhsengland.github.io/evalsense/guide. (b) The web-based user interface provided by the EvalSense library can be used to configure and execute evaluations, as well as to view their results. Alternatively, this can be done through code after importing the library.