TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains

• URL: http://arxiv.org/abs/2410.11507v5
• Date: Thu, 25 Sep 2025 10:19:24 GMT
• Title: TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains
• Authors: Wanying Wang, Zeyu Ma, Xuhong Wang, Yangchun Zhang, Pengfei Liu, Mingang Chen,
• Abstract summary: 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.
• Score: 19.492393243160244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Large Language Models (LLMs) are increasingly deployed in highly specialized vertical domains, the evaluation of their domain-specific performance becomes critical. However, existing evaluations for vertical domains typically rely on the labor-intensive construction of static single-turn datasets, which present two key limitations: (i) manual data construction is costly and must be repeated for each new domain, and (ii) static single-turn evaluations are misaligned with the dynamic multi-turn interactions in real-world applications, limiting the assessment of professionalism and stability. To address these, we propose TestAgent, a framework for automatic benchmarking and exploratory dynamic evaluation in vertical domains. TestAgent leverages retrieval-augmented generation to create domain-specific questions from user-provided knowledge sources, combined with a two-stage criteria generation process, thereby enabling scalable and automated benchmark creation. Furthermore, it introduces a reinforcement learning-guided multi-turn interaction strategy that adaptively determines question types based on real-time model responses, dynamically probing knowledge boundaries and stability. Extensive experiments across medical, legal, and governmental domains demonstrate that TestAgent enables efficient cross-domain benchmark generation and yields deeper insights into model behavior through dynamic exploratory evaluation. This work establishes a new paradigm for automated and in-depth evaluation of LLMs in vertical domains.

Related papers

• Automated Benchmark Generation from Domain Guidelines Informed by Bloom's Taxonomy [28.293009223912602]
  Open-ended question answering (QA) evaluates a model's ability to perform contextualized reasoning beyond factual recall.<n>This challenge is especially acute in practice-based domains, where knowledge is procedural and grounded in professional judgment.<n>We introduce a framework for automated benchmark generation from expert-authored guidelines informed by Bloom's taxonomy.
  arXiv  Detail & Related papers  (2026-01-28T05:01:11Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• REVELIO -- Universal Multimodal Task Load Estimation for Cross-Domain Generalization [2.689067085628911]
  This paper introduces a new multimodal dataset that extends established cognitive load detection benchmarks with a real-world gaming application.<n>Task load annotations are derived from objective performance, subjective NASA-TLX ratings, and task-level design.<n>State-of-the-art end-to-end model, including xLSTM, ConvNeXt, and Transformer architectures are systematically trained and evaluated.
  arXiv  Detail & Related papers  (2025-09-01T17:36:09Z)
• MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers [86.00932417210477]
  We introduce MCP-Universe, the first comprehensive benchmark specifically designed to evaluate LLMs in realistic and hard tasks through interaction with real-world MCP servers.<n>Our benchmark encompasses 6 core domains spanning 11 different MCP servers: Location Navigation, Repository Management, Financial Analysis, 3D Design, Browser Automation, and Web Searching.<n>We find that even SOTA models such as GPT-5 (43.72%), Grok-4 (33.33%) and Claude-4.0-Sonnet (29.44%) exhibit significant performance limitations.
  arXiv  Detail & Related papers  (2025-08-20T13:28:58Z)
• 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)
• ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation [51.297873393639456]
  ArtifactsBench is a framework for automated visual code generation evaluation.<n>Our framework renders each generated artifact and captures its dynamic behavior through temporal screenshots.<n>We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading Large Language Models.
  arXiv  Detail & Related papers  (2025-07-07T12:53:00Z)
• 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)
• REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites [9.58858258192147]
  We introduce REAL, a benchmark and framework for multi-turn agent evaluations on deterministic simulations of real-world websites.<n>We also release a benchmark consisting of 112 practical tasks that mirror everyday complex user interactions.<n>Our framework supports easy integration of new tasks, reproducible evaluation, and scalable post-training data generation.
  arXiv  Detail & Related papers  (2025-04-15T18:22:55Z)
• 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.