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

• URL: http://arxiv.org/abs/2602.18823v1
• Date: Sat, 21 Feb 2026 12:50:43 GMT
• Title: EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation
• Authors: Adam Dejl, Jonathan Pearson,
• Abstract summary: We present EvalSense, a framework for constructing domain-specific evaluation suites for large language models (LLMs)<n>EvalSense provides out-of-the-box support for a broad range of model providers and evaluation strategies.<n>We demonstrate the effectiveness of EvalSense in a case study involving the generation of clinical notes from unstructured doctor-patient dialogues.
• Score: 1.9336815376402718
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models [76.72220653705679]
  We introduce MCPEval, an open-source framework that automates end-to-end task generation and deep evaluation of intelligent agents.<n> MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines.<n> Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance.
  arXiv  Detail & Related papers  (2025-07-17T05:46:27Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey [64.08485471150486]
  This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings.<n>We systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication.
  arXiv  Detail & Related papers  (2025-03-28T14:08:40Z)
• SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection [70.23196257213829]
  We propose a scalable and reliable Semantic-level Evaluation framework for Open domain Event detection.<n>Our proposed framework first constructs a scalable evaluation benchmark that currently includes 564 event types covering 7 major domains.<n>We then leverage large language models (LLMs) as automatic evaluation agents to compute a semantic F1-score, incorporating fine-grained definitions of semantically similar labels.
  arXiv  Detail & Related papers  (2025-03-05T09:37:05Z)
• PanguIR Technical Report for NTCIR-18 AEOLLM Task [12.061652026366591]
  Large language models (LLMs) are increasingly critical and challenging to evaluate.<n>Manual evaluation, while comprehensive, is often costly and resource-intensive.<n>automatic evaluation offers greater scalability but is constrained by the limitations of its evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-04T07:40:02Z)
• OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain [62.89809156574998]
  We introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain.<n>Our benchmark is characterized by its multi-dimensional evaluation framework.<n>Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets.
  arXiv  Detail & Related papers  (2024-12-17T15:38:42Z)
• SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts [0.6291443816903801]
  This paper introduces a novel framework designed to autonomously evaluate the robustness of large language models (LLMs)<n>Our method generates descriptive sentences from domain-constrained knowledge graph triplets to formulate adversarial prompts.<n>This self-evaluation mechanism allows the LLM to evaluate its robustness without the need for external benchmarks.
  arXiv  Detail & Related papers  (2024-12-01T10:58:53Z)
• TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains [19.492393243160244]
  Large Language Models (LLMs) are increasingly deployed in highly specialized vertical domains.<n>Existing evaluations for vertical domains typically rely on the labor-intensive construction of static single-turn datasets.<n>We propose TestAgent, a framework for automatic benchmarking and exploratory dynamic evaluation in vertical domains.
  arXiv  Detail & Related papers  (2024-10-15T11:20:42Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models [36.273451767886726]
  FreeEval is a modular and scalable framework crafted to enable trustworthy and efficient automatic evaluations of large language models. FreeEval's unified abstractions simplify the integration and improve the transparency of diverse evaluation methodologies. The framework integrates meta-evaluation techniques like human evaluation and data contamination detection, which, along with dynamic evaluation modules, enhance the fairness of the evaluation outcomes.
  arXiv  Detail & Related papers  (2024-04-09T04:17:51Z)
• Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate [74.06294042304415]
  We propose ScaleEval, an agent-debate-assisted meta-evaluation framework. We release the code for our framework, which is publicly available on GitHub.
  arXiv  Detail & Related papers  (2024-01-30T07:03:32Z)
• LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models [28.441725610692714]
  We propose a unified multi-dimensional automatic evaluation method for open-domain conversations with large language models (LLMs) We design a single prompt-based evaluation method that leverages a unified evaluation schema to cover multiple dimensions of conversation quality in a single model call. We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods.
  arXiv  Detail & Related papers  (2023-05-23T05:57:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.