Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework

• URL: http://arxiv.org/abs/2507.08459v1
• Date: Fri, 11 Jul 2025 10:02:21 GMT
• Title: Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
• Authors: Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin,
• Abstract summary: We establish a Misattribution Framework with 6 primary and 15 secondary categories to facilitate in-depth analysis.<n>We present AttriData, a dataset specifically designed for error attribution, encompassing misattribution, along with the corresponding scores and feedback.<n>We also propose MisAttributionLLM, a fine-tuned model on AttriData, which is the first general-purpose judge model capable of simultaneously generating score, misattribution, and feedback.
• Score: 2.0364208478403554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated framework to systematically categorize and attribute errors. However, existing evaluation models lack error attribution capability. In this work, we establish a comprehensive Misattribution Framework with 6 primary and 15 secondary categories to facilitate in-depth analysis. Based on this framework, we present AttriData, a dataset specifically designed for error attribution, encompassing misattribution, along with the corresponding scores and feedback. We also propose MisAttributionLLM, a fine-tuned model on AttriData, which is the first general-purpose judge model capable of simultaneously generating score, misattribution, and feedback. Extensive experiments and analyses are conducted to confirm the effectiveness and robustness of our proposed method.

Related papers

• CLEAR: Error Analysis via LLM-as-a-Judge Made Easy [9.285203198113917]
  We introduce CLEAR, an interactive, open-source package for LLM-based error analysis.<n> CLEAR first generates per-instance textual feedback, then creates a set of system-level error issues, and quantifies the prevalence of each identified issue.<n>Our package also provides users with an interactive dashboard that allows for a comprehensive error analysis through aggregate visualizations.
  arXiv  Detail & Related papers  (2025-07-24T13:15:21Z)
• Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis [36.689210473887904]
  We introduce a benchmarking framework for evaluating cross-dataset prediction generalization in deep learning (DL) and machine learning (ML) models.<n>We quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results)<n>Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments.
  arXiv  Detail & Related papers  (2025-03-18T15:40:18Z)
• 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)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation [18.77296551727931]
  We propose DECIDER, a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image models. DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-08-01T07:08:11Z)
• AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding [29.07617945233152]
  Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance.<n>This approach faces significant challenges, including the laborious and costly requirement for additional metadata.<n>We introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for metadata-free data slice finding.<n>Our system identifies interpretable data slices that involve common model behaviors and visualizes these patterns through an Attribution Mosaic design.
  arXiv  Detail & Related papers  (2024-01-12T09:17:32Z)
• Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization [34.85353544844499]
  We present the first dataset with fine-grained factual error annotations named DIASUMFACT. We define fine-grained factual error detection as a sentence-level multi-label classification problem. We propose an unsupervised model ENDERANKER via candidate ranking using pretrained encoder-decoder models.
  arXiv  Detail & Related papers  (2023-05-26T00:18:33Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• Generalization Properties of Retrieval-based Models [50.35325326050263]
  Retrieval-based machine learning methods have enjoyed success on a wide range of problems. Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored. We present a formal treatment of retrieval-based models to characterize their generalization ability.
  arXiv  Detail & Related papers  (2022-10-06T00:33:01Z)
• How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models [95.8037674226622]
  We introduce a 3-dimensional evaluation metric that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity.
  arXiv  Detail & Related papers  (2021-02-17T18:25:30Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)

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.