Towards Automated Error Discovery: A Study in Conversational AI

• URL: http://arxiv.org/abs/2509.10833v1
• Date: Sat, 13 Sep 2025 14:53:22 GMT
• Title: Towards Automated Error Discovery: A Study in Conversational AI
• Authors: Dominic Petrak, Thy Thy Tran, Iryna Gurevych,
• Abstract summary: We introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI.<n>We also propose SEEED (Soft Clustering Extended-Based Error Detection), as an encoder-based approach to its implementation.
• Score: 48.735443116662026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching users during deployment. Recent research leverages large language models (LLMs) to detect errors and guide response-generation models toward improvement. However, current LLMs struggle to identify errors not explicitly specified in their instructions, such as those arising from updates to the response-generation model or shifts in user behavior. In this work, we introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI, and propose SEEED (Soft Clustering Extended Encoder-Based Error Detection), as an encoder-based approach to its implementation. We enhance the Soft Nearest Neighbor Loss by amplifying distance weighting for negative samples and introduce Label-Based Sample Ranking to select highly contrastive examples for better representation learning. SEEED outperforms adapted baselines -- including GPT-4o and Phi-4 -- across multiple error-annotated dialogue datasets, improving the accuracy for detecting unknown errors by up to 8 points and demonstrating strong generalization to unknown intent detection.

Related papers

• EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI [36.91800117379075]
  EVADE is a framework for generating and validating explanations to detect errors using large language models.<n>HLV arises when multiple labels are valid for the same instance, making it difficult to separate annotation errors from plausible variation.
  arXiv  Detail & Related papers  (2025-11-12T03:49:05Z)
• DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models [60.713908578319256]
  We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
  arXiv  Detail & Related papers  (2025-09-15T10:59:57Z)
• Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings [9.763273544617176]
  Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning.<n>In this paper, we introduce a simple yet effective framework to address this challenge.<n>Our approach is specifically designed for per-utterance classification problems, which encompass tasks such as intent detection, dialogue state tracking, and more.
  arXiv  Detail & Related papers  (2025-03-07T17:46:13Z)
• IterPref: Focal Preference Learning for Code Generation via Iterative Debugging [28.020886216989872]
  We propose IterPref, a new preference alignment framework for Code LLMs.<n>IterPref explicitly locates error regions and aligns the corresponding tokens via a tailored DPO algorithm.<n>IterPref achieves significant performance gains in code generation and improves on challenging tasks like BigCodeBench.
  arXiv  Detail & Related papers  (2025-03-04T16:56:34Z)
• Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework [79.40678802098026]
  Math Word Problems serve as a crucial benchmark for evaluating Large Language Models' reasoning abilities.<n>Current error classification methods rely on static and predefined categories.<n>We propose Error-Aware Prompting (EAP) that incorporates common error patterns as explicit guidance.
  arXiv  Detail & Related papers  (2025-01-26T16:17:57Z)
• Palisade -- Prompt Injection Detection Framework [0.9620910657090188]
  Large Language Models are vulnerable to malicious prompt injection attacks. This paper proposes a novel NLP based approach for prompt injection detection. It emphasizes accuracy and optimization through a layered input screening process.
  arXiv  Detail & Related papers  (2024-10-28T15:47:03Z)
• Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing [59.405145971637204]
  We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE)<n>RISE injects predefined subtle errors into pivotal tokens in reasoning or steps to construct hard pairs for error mitigation.<n>Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH with only 4.5K training samples.
  arXiv  Detail & Related papers  (2024-10-09T07:43:38Z)
• 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)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• 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)

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.