Papers with error detection
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection (2025.acl-industry)
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| Challenge: | Multimodal Large Language Models (MLLMs) struggle with identifying and categorizing student errors in multimodal mathematical contexts. |
| Approach: | They propose a new framework that decomposes error detection into three phases with specialized agents. |
| Outcome: | The proposed framework shows higher accuracy in error step identification and 3% improvement in error categorization on real-world educational data. |
Sprucing up the trees – Error detection in treebanks (C18-1)
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| Challenge: | a method for detecting annotation errors in manually annotated dependency trees is presented . the method is based on ensemble parsing and Bayesian inference guided by active learning . |
| Approach: | They propose a method for detecting annotation errors in manually annotated dependency parse trees . they use ensemble parsing in combination with Bayesian inference guided by active learning . |
| Outcome: | The proposed method detects errors in annotated dependency treebanks and improves parsing accuracy on in- and out-of-domain data. |
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm (D19-55)
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| Challenge: | Existing spell checkers for Chinese are based on denoising autoencoder and decoder paradigms that require a small amount of data to be effective. |
| Approach: | They propose a Chinese spell checker based on a new paradigm which consists of a denoising autoencoder and a decoder. |
| Outcome: | The proposed spell checker is faster, more Adaptable to simplified and traditional Chinese texts and has a much simpler structure to be as much Powerful in error detection and correction. |
Is my Meeting Summary Good? Estimating Quality with a Multi-LLM Evaluator (2025.coling-industry)
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| Challenge: | Established metrics such as ROUGE and BERTScore have a relatively low correlation with human judgments and fail to capture nuanced errors. |
| Approach: | They propose a framework that uses a three-step assessment of individual error types, multi-agent discussion for decision refinement, and feedback-based self-training to refine error definition understanding and alignment with human judgment. |
| Outcome: | The proposed framework achieves high correlation with human judgment and a consistent rating and adaptability to custom error guidelines. |
CodeGenWrangler: Data Wrangling task automation using Code-Generating Models (2025.naacl-industry)
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
| Approach: | They propose a system that leverages large language models to generate executable code for data wrangling tasks . they identify inherent patterns in the data while leveraging external knowledge . |
| Outcome: | The proposed system detects patterns in the data while leveraging external knowledge . it generates executable code for data-wrangling tasks like missing value imputation and error correction . |
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)
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| Challenge: | Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs). |
| Approach: | They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs. |
| Outcome: | The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting. |
Spelling Error Correction with Soft-Masked BERT (2020.acl-main)
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| Challenge: | Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT. |
| Approach: | They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique. |
| Outcome: | The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems. |
Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use. |
| Approach: | They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues. |
| Outcome: | The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior. |
Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios? (2024.findings-acl)
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| Challenge: | Existing games such as "Who is undercover" are subjective and difficult to evaluate . |
| Approach: | They propose a game called BrainKing that evaluates LLMs' problem-solving capability under incomplete information scenarios. |
| Outcome: | The proposed game requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers. |
Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context (2024.lrec-main)
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| Challenge: | Using an automatic annotation toolkit, we evaluated the performance of the sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students’ writing samples. |
| Approach: | They evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model using Japanese university students’ writing samples. |
| Outcome: | The proposed model shows a high precision but conservativeness in error detection and correction. |
Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)
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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge. |
| Approach: | They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods. |
| Outcome: | The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets. |
BacktrackAgent: Enhancing GUI Agent with Error Detection and Backtracking Mechanism (2025.emnlp-main)
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| Challenge: | Existing GUI agents focus on enhancing the accuracy of individual actions and lack effective mechanisms for detecting and recovering from errors. |
| Approach: | They propose a framework that incorporates a backtracking mechanism to enhance the task completion capabilities of GUI agents by verifier, judger, and reflector components. |
| Outcome: | The proposed framework improves task success rate and step accuracy on Mobile3M and Auto-UI benchmarks. |
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs (2025.emnlp-main)
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Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing detection methods fail to account for **self-consistent error** . study identifies self-consistency errors and evaluates them . |
| Approach: | They propose a method that fuses hidden state evidence from an external verifier LLM to detect self-consistent errors. |
| Outcome: | The proposed method significantly enhances performance on self-consistent errors across three LLM families. |
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors (N19-1)
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| Challenge: | a recent study shows that neural sequential labelers overfit their training data to detect SVA errors. |
| Approach: | They propose a simple protocol that generates a neural sequential labeler from silver standard data and gold standard data. |
| Outcome: | The proposed method leads to more robust detection of SVA errors on silver standard data and gold standard data. |
SPE Attention: Making Attention Equivariant to Semantic-Preserving Permutation for Code Processing (2025.emnlp-main)
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| Challenge: | Existing approaches to train code processing models to capture symmetry of code . semantic-preserving permutations are not found in natural languages . |
| Approach: | They propose a mechanism that captures a unique symmetry of code, called the SPE attention . they propose symmetry graphs that are then combined to create a symmetry mask . |
| Outcome: | The proposed model can be used to analyze code summarization and error detection tasks. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
Know What I don’t Know: Handling Ambiguous and Unknown Questions for Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL parsers generate a plausible SQL query for arbitrary user questions, thereby failing to handle problematic user questions. |
| Approach: | They propose a weakly supervised DTE model for error detection, localization, and explanation. |
| Outcome: | The proposed model achieves the best result on real-world examples and generated examples compared with baselines. |
VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning (2025.findings-naacl)
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| Challenge: | Existing approaches to enhance large language models' ability to predict program behavior struggle with dynamic reasoning tasks. |
| Approach: | They propose a visual control flow graph that integrates CoT reasoning with a control flow . they aim to improve performance in program behavior prediction, error detection and output generation . |
| Outcome: | The proposed approach improves program behavior prediction, error detection, and output generation. |
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
| Outcome: | The proposed model performs on par with the state-of-the-art models while requiring fewer resources. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
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| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models (2021.acl-long)
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| Challenge: | Using pretrained language models, we propose an error-annotated dataset for text generation . we use carefully selected prompt words to guide GPT-2 to generate candidate sentences . |
| Approach: | They propose an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models. |
| Outcome: | The proposed dataset covers 24 types of errors according to common sense and linguistics. |
String Editing Based Chinese Grammatical Error Diagnosis (2022.coling-1)
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| Challenge: | Chinese Grammatical Error Diagnosis (CGED) suffers from numerous types of grammatical errors and insufficiency of training data. |
| Approach: | They propose a string editing based CGED model that uses a unified workflow to handle various types of grammatical errors. |
| Outcome: | The proposed model outperforms existing models on Chinese datasets in many aspects. |
Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for large language models overlook the inherent randomness of LLMs. |
| Approach: | They propose a hierarchical statistical model that incorporates both benchmark characteristics and LLM randomness to provide a more comprehensive representation of benchmarking process. |
| Outcome: | The proposed model improves the accuracy of estimating the benchmark score and reduces variance. |
Developing a Spell and Grammar Checker for Icelandic using an Error Corpus (2022.lrec-1)
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| Challenge: | lack of datasets for spelling and grammar correction in Icelandic has caused a dearth of spell and grammar checking systems for the low-resource language. |
| Approach: | They propose to use an error corpus to guide the development of a spell and grammar checking tool for Icelandic, using tokenizer, tagger, and parser. |
| Outcome: | The proposed system is based on an error corpus at all stages of development and is reproducible for other low- or medium-resource languages and especially well-suited to morphologically rich languages. |
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration (2025.naacl-long)
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| Challenge: | Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. |
| Approach: | They propose a multi-agent multi-model reasoning recipe to improve faithfulness through refinement. |
| Outcome: | The proposed method improves faithfulness and error detection on three summarization datasets and on long-form question-answering tasks. |
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing approaches to self-correct text-to-SQL fail to demonstrate underlying reasoning path . authors propose **SHARE**, a self-revolution assistant for text-based error correction . |
| Approach: | They propose a "SHARE" assistant that enables LLMs to perform more precise error localization and efficient correction. |
| Outcome: | The proposed assistant performs more precise error localization and efficient correction for monolithic SQL queries. |
MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations (2026.findings-acl)
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Congbo Ma, Yichun Zhang, Yousef Al-Jazzazi, Ahamed Foisal, Laasya Sharma, Yousra Sadqi, Khaled Saleh, Jihad Mallat, Farah E. Shamout
| Challenge: | Existing or generated clinical text may contain inaccuracies that can lead to serious adverse outcomes. |
| Approach: | They introduce a multilingual benchmark for error detection, localization and correction . they assessed the performance of a range of general-purpose, language-specific, and medical-domain language models . |
| Outcome: | The proposed benchmark covers English, Arabic and Chinese, with natural medical cases annotated and reviewed by domain experts. |
A Tale of Evaluating Factual Consistency: Case Study on Long Document Summarization Evaluation (2025.findings-acl)
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| Challenge: | Despite the recent progress for summarization models in producing fluent summaries, they still encounter challenges when long sequences of generated texts and inputs (over thousands of words) need to be evaluated. |
| Approach: | They conduct a systematic analysis of factual-consistency evaluation systems across four long-document datasets and examine the relationship between sentence-level and summary-level model performance. |
| Outcome: | The proposed models can achieve higher recall in error detection for older summaries, yet struggle with false positives and fine-grained error detection. |
Can Vision-Language Models Evaluate Handwritten Math? (2025.acl-long)
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| Challenge: | Recent advances in Vision-Language Models (VLMs) have significantly enhanced the ability to interpret both textual and visual data. |
| Approach: | They propose a benchmark to assess VLMs’ ability to detect, localize and correct errors in handwritten mathematical content. |
| Outcome: | The proposed benchmark covers over 2,200 handwritten math solutions from 609 manually curated problems from grades 7-12 with intentionally introduced perturbations. |
Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are prone to hallucination, especially during multihop tasks. |
| Approach: | They propose a hierarchical, erroraware discriminative PRM that classifies math errors at each step and combines finegrained signals to estimate step correctness. |
| Outcome: | The proposed model outperforms the prior best in a new stateof-theart PRMScore of 67.7 on a 400Ksample dataset . |
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) exhibit unexpected failures in which they are confidently incorrect. |
| Approach: | They propose a method for calibrating SQL outputs that leverages structured nature to provide more granular signals of correctness. |
| Outcome: | The proposed method improves on two popular text-to-SQL datasets and provides a confidence score that is calibrated. |
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Correction (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. |
| Approach: | They propose a benchmark to assess LLMs on error detection and correction in molecular descriptions. |
| Outcome: | The proposed benchmark targets LLMs on error detection and correction in molecular descriptions. |
MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification (2025.findings-acl)
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xu Zhao Pan, Pengfei Zhou, Jiaxin Ai, Wangbo Zhao, Kai Wang, Xiaojiang Peng, Wenqi Shao, Hongxun Yao, Kaipeng Zhang
| Challenge: | Existing benchmarks of large language models focus on error detection, neglecting other scenarios like reasoning search. |
| Approach: | et al. propose a multi-task, multimodal benchmark to assess effectiveness of PRMs . step correctness, answers aggregation and reasoning process search are evaluated . ethical principles of MPBench are based on a set of evaluation paradigms based in a text-based benchmark . |
| Outcome: | a new benchmark assesses the effectiveness of large language models (LLMs) in multiple scenarios . it uses three evaluation paradigms to assess the effectiveness and compares them with existing models . a the proposed model improves reasoning accuracy by providing stepwise feedback for multi-step reasoning results . |
Dyve: Thinking Fast and Slow for Dynamic Process Verification (2025.emnlp-main)
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| Challenge: | Existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. |
| Approach: | They propose a dynamic process verifier that integrates fast and slow thinking to enhance reasoning error detection in large language models. |
| Outcome: | The proposed system outperforms existing process-based verifiers and maintains computational efficiency while maintaining high performance. |
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes (2025.findings-acl)
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| Challenge: | Several studies have shown that large language models can answer medical questions correctly, outperforming the average human score in some medical exams. |
| Approach: | They introduce MEDEC, the first publicly available benchmark for medical error detection and correction in clinical notes. |
| Outcome: | The proposed model outperforms medical doctors in errors detection and correction tasks. |
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)
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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen
| Challenge: | Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection. |
| Approach: | They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization. |
| Outcome: | The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models. |
Step Feasibility-Aware and Error-Correctable Entailment Tree Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating entailment trees suffer from false feasible steps, resulting in error propagation. |
| Approach: | They propose an iterative entailment tree generation framework with step feasibility perception and state error handling mechanisms to enhance the interpretability of QA systems. |
| Outcome: | The proposed framework improves the interpretation of QA systems by demonstrating that it is feasible to choose steps that are false feasible and error propagating. |
The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It (2025.emnlp-main)
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| Challenge: | Existing studies have explored methods to enhance self-correction in large language models, but little attention has been given to understanding the models’ internal mechanisms underlying error detection. |
| Approach: | They propose to use a large language model to analyze arithmetic errors in four smaller-sized LLMs and identify their internal mechanisms. |
| Outcome: | The proposed models heavily rely on consistency headstextemdashattention heads that assess surface-level alignment of numerical values in arithmetic solutions. |
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors (2025.emnlp-main)
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| Challenge: | Existing methods for error or uncertainty estimation rely on logits, entropy, sampling variance. |
| Approach: | They propose an input-only method for anticipating language model errors using token-level likelihood features inspired by surprisal and the Uniform Information Density hypothesis. |
| Outcome: | The proposed method outperforms baseline models and standard models on linguistic datasets. |