TestAgent: A Framework for Domain-Adaptive Evaluation of LLMs via Dynamic Benchmark Construction and Exploratory Interaction

• URL: http://arxiv.org/abs/2410.11507v4
• Date: Fri, 16 May 2025 05:34:13 GMT
• Title: TestAgent: A Framework for Domain-Adaptive Evaluation of LLMs via Dynamic Benchmark Construction and Exploratory Interaction
• Authors: Wanying Wang, Zeyu Ma, Pengfei Liu, Mingang Chen,
• Abstract summary: Large language models (LLMs) are increasingly deployed to various vertical domains.<n>Current evaluation methods rely on static and resource-intensive datasets that are not aligned with real-world requirements.<n>We introduce two key concepts: textbfBenchmark+, which extends the traditional question-answer benchmark into a more flexible strategy-criterion'' format.<n>We propose textbftextscTestAgent, an agent-based evaluation framework that implements these concepts using retrieval-augmented generation and reinforcement learning.
• Score: 29.72874725703848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) are increasingly deployed to various vertical domains, automatically evaluating their performance across different domains remains a critical challenge. Current evaluation methods often rely on static and resource-intensive datasets that are not aligned with real-world requirements and lack cross-domain adaptability. To address these limitations, we revisit the evaluation process and introduce two key concepts: \textbf{Benchmark+}, which extends the traditional question-answer benchmark into a more flexible ``strategy-criterion'' format; and \textbf{Assessment+}, which enhances the interaction process to facilitate deeper exploration and comprehensive analysis from multiple perspectives. We propose \textbf{\textsc{TestAgent}}, an agent-based evaluation framework that implements these concepts using retrieval-augmented generation and reinforcement learning. \textsc{TestAgent} enables automatic dynamic benchmark generation and in-depth assessment across diverse vertical domains. Experiments on tasks ranging from constructing multiple vertical domain evaluations to transforming static benchmarks into dynamic forms demonstrate the effectiveness of \textsc{TestAgent}. This work provides a novel perspective on automatic evaluation methods for domain-specific LLMs, offering a pathway for domain-adaptive dynamic benchmark construction and exploratory assessment.

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)
• Beyond Benchmarks: A Novel Framework for Domain-Specific LLM Evaluation and Knowledge Mapping [0.7555681642774916]
  The paper addresses two critical challenges in language model (LM) evaluation: creating reliable domain-specific benchmarks and understanding knowledge representation during domain adaptation.<n>We introduce a deterministic pipeline that converts raw domain corpora into completion-type benchmarks without relying on LMs or human curation.<n>Our approach generates domain-specific keywords and related word lists using TF and Term TF-IDF methods and constructs prompt-target pairs.<n>We evaluate models by measuring their ability to complete these prompts with the correct domain-specific targets, providing a direct assessment of domain knowledge with low computational cost.
  arXiv  Detail & Related papers  (2025-06-09T11:30:12Z)
• Survey on Evaluation of LLM-based Agents [28.91672694491855]
  The emergence of LLM-based agents represents a paradigm shift in AI. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable agents.
  arXiv  Detail & Related papers  (2025-03-20T17:59:23Z)
• 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. Our proposed framework first constructs a scalable evaluation benchmark that currently includes 564 event types covering 7 major domains. 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)
• Dynamic benchmarking framework for LLM-based conversational data capture [0.0]
  This paper introduces a benchmarking framework to assess large language models (LLMs) It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement. Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
  arXiv  Detail & Related papers  (2025-02-04T15:47:47Z)
• 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. Our benchmark is characterized by its multi-dimensional evaluation framework. Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets.
  arXiv  Detail & Related papers  (2024-12-17T15:38:42Z)
• EvalGIM: A Library for Evaluating Generative Image Models [26.631349186382664]
  We introduce EvalGIM, a library for evaluating text-to-image generative models. EvalGIM contains broad support for datasets and metrics used to measure quality, diversity, and consistency. EvalGIM also contains Evaluation Exercises that introduce two new analysis methods for text-to-image generative models.
  arXiv  Detail & Related papers  (2024-12-13T23:15:35Z)
• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature. We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates [0.0]
  We propose a framework that interprets large language models (LLMs) as advocates within an ensemble of interacting agents. This approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics.
  arXiv  Detail & Related papers  (2024-10-07T00:22:07Z)
• RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models [5.0741409008225755]
  Large language models (LLMs) have emerged as promising tools for solving challenging robotic tasks. Most existing LLM-based agents lack the ability to retain and learn from past interactions. We propose RAG-Modulo, a framework that enhances LLM-based agents with a memory of past interactions and incorporates critics to evaluate the agents' decisions.
  arXiv  Detail & Related papers  (2024-09-18T20:03:32Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation [51.99752147380505]
  This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs) We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence. Our framework widens performance discrepancies both between different models and within the same model across various tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:40:06Z)
• F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods [102.98899881389211]
  We propose F-Eval, a bilingual evaluation benchmark to evaluate the fundamental abilities, including expression, commonsense and logic. For reference-free subjective tasks, we devise new evaluation methods, serving as alternatives to scoring by API models.
  arXiv  Detail & Related papers  (2024-01-26T13:55:32Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [76.95062553043607]
  evaluating large language models (LLMs) is essential for understanding their capabilities and facilitating their integration into practical applications. We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)
• Don't Make Your LLM an Evaluation Benchmark Cheater [142.24553056600627]
  Large language models(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs. We discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.
  arXiv  Detail & Related papers  (2023-11-03T14:59:54Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction [67.54420015049732]
  Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. Existing benchmarks are limited to two domains and do not evaluate model performance on unseen domains. We introduce a domain-expanded benchmark by annotating samples from diverse domains, enabling evaluation of models in both in-domain and out-of-domain settings.
  arXiv  Detail & Related papers  (2023-05-23T18:01:49Z)

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.