LITE: LLM-Impelled efficient Taxonomy Evaluation

• URL: http://arxiv.org/abs/2504.01369v1
• Date: Wed, 02 Apr 2025 05:33:05 GMT
• Title: LITE: LLM-Impelled efficient Taxonomy Evaluation
• Abstract summary: LITE is a top-down hierarchical taxonomy evaluation strategy, breaking down the taxonomy into manageable substructures.<n>It provides both quantitative performance analysis and qualitative insights by integrating evaluation metrics closely aligned with task objectives.
• Score: 32.8374687916129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents LITE, an LLM-based evaluation method designed for efficient and flexible assessment of taxonomy quality. To address challenges in large-scale taxonomy evaluation, such as efficiency, fairness, and consistency, LITE adopts a top-down hierarchical evaluation strategy, breaking down the taxonomy into manageable substructures and ensuring result reliability through cross-validation and standardized input formats. LITE also introduces a penalty mechanism to handle extreme cases and provides both quantitative performance analysis and qualitative insights by integrating evaluation metrics closely aligned with task objectives. Experimental results show that LITE demonstrates high reliability in complex evaluation tasks, effectively identifying semantic errors, logical contradictions, and structural flaws in taxonomies, while offering directions for improvement. Code is available at https://github.com/Zhang-l-i-n/TAXONOMY_DETECT .

Related papers

• (Towards) Scalable Reliable Automated Evaluation with Large Language Models [0.0]
  This work introduces a novel evaluation framework designed to approximate expert-level assessments of Large Language Models (LLMs) outputs.<n>An Elo rating system is used to generate stable and interpretable rankings.<n>Preliminary results show that automatically derived rankings correlate well with expert judgments.
  arXiv  Detail & Related papers  (2026-07-30T14:31:07Z)
• BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration [6.719751155411076]
  BoostTaxo is a boosting-style LLM framework for zero-shot taxonomy induction.<n>It takes a set of domain terms as inputs and performs parent identification in a coarse-to-fine manner.<n>It employs retrieval-augmented definition refinement, hybrid parent candidate selection, candidate rating, and structure-aware score calibration.<n>It achieves superior or comparable performance to state-of-the-art methods in zero-shot taxonomy induction.
  arXiv  Detail & Related papers  (2026-04-03T15:48:09Z)
• Evaluating LLM-Based Grant Proposal Review via Structured Perturbations [18.689211845609623]
  We develop a perturbation-based framework probing LLM sensitivity across six quality axes.<n>We compare three review architectures: single-pass review, section-by-section analysis, and a 'Council of Personas' ensemble emulating expert panels.
  arXiv  Detail & Related papers  (2026-03-09T11:53:50Z)
• WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality [62.43165871914528]
  We introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development.<n>WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics.<n>In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias.
  arXiv  Detail & Related papers  (2025-10-21T12:16:04Z)
• CRACQ: A Multi-Dimensional Approach To Automated Document Assessment [0.0]
  CRACQ is a multi-dimensional evaluation framework tailored to evaluate documents across f i v e specific traits: Coherence, Rigor, Appropriateness, Completeness, and Quality.<n>It integrates linguistic, semantic, and structural signals into a cumulative assessment, enabling both holistic and trait-level analysis.
  arXiv  Detail & Related papers  (2025-09-26T17:01:54Z)
• STEM: Efficient Relative Capability Evaluation of LLMs through Structured Transition Samples [3.41981716024098]
  evaluating large language models (LLMs) has become increasingly challenging as model capabilities advance rapidly.<n>We propose the textbfStructured textbfTransition textbfEvaluation textbfMethod (STEM) as a lightweight and interpretable evaluation framework.
  arXiv  Detail & Related papers  (2025-08-16T16:36:43Z)
• An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability [2.8948274245812327]
  We study the effects of evaluation design, decoding strategies, and Chain-of-Tought (CoT) reasoning in evaluation.<n>Our results show that evaluation criteria are critical for reliability, non-deterministic sampling improves alignment with human preferences over deterministic evaluation, and CoT reasoning offers minimal gains when clear evaluation criteria are present.
  arXiv  Detail & Related papers  (2025-06-16T16:04:43Z)
• 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)
• FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models [79.41859481668618]
  Large Language Models (LLMs) have significantly advanced the fact-checking studies.<n>Existing automated fact-checking evaluation methods rely on static datasets and classification metrics.<n>We introduce FACT-AUDIT, an agent-driven framework that adaptively and dynamically assesses LLMs' fact-checking capabilities.
  arXiv  Detail & Related papers  (2025-02-25T07:44:22Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions.<n>Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• Enabling Scalable Oversight via Self-Evolving Critic [59.861013614500024]
  SCRIT (Self-evolving CRITic) is a framework that enables genuine self-evolution of critique abilities. It self-improves by training on synthetic data, generated by a contrastive-based self-critic. It achieves up to a 10.3% improvement on critique-correction and error identification benchmarks.
  arXiv  Detail & Related papers  (2025-01-10T05:51:52Z)
• StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
  StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs.<n> Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets.<n>We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
  arXiv  Detail & Related papers  (2024-12-23T22:08:40Z)
• The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? [1.3810901729134184]
  Large Language Models (LLMs) excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum. We lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks.
  arXiv  Detail & Related papers  (2024-12-02T20:49:21Z)
• Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework [2.4861619769660637]
  We propose an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations. We demonstrate how the framework can help uncover underlying issues, their causes, and potential solutions.
  arXiv  Detail & Related papers  (2024-06-14T18:47:37Z)
• 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)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists [12.542045913426639]
  CheckEval is a checklist-based evaluation framework that improves rating reliability via binary questions.<n>CheckEval dramatically improves the average agreement across evaluator models by 0.45 and reduces the score variance.
  arXiv  Detail & Related papers  (2024-03-27T17:20:39Z)
• Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach [64.42462708687921]
  Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods. This includes the application of ANOVA, Tukey HSD tests, GAMM, and clustering technique.
  arXiv  Detail & Related papers  (2024-03-22T14:47:35Z)
• DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models [4.953092503184905]
  This work proposes DCR, an automated framework for evaluating and improving the consistency of Large Language Models (LLMs) generated texts. We introduce an automatic metric converter (AMC) that translates the output from DCE into an interpretable numeric score. Our approach also substantially reduces nearly 90% of output inconsistencies, showing promise for effective hallucination mitigation.
  arXiv  Detail & Related papers  (2024-01-04T08:34:16Z)
• MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
  We introduce a novel evaluation paradigm for Large Language Models (LLMs) This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
  arXiv  Detail & Related papers  (2023-12-28T15:49:43Z)

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.