HALLUCINATION DETECTION

Uncertainty Quantification for Large Language Models (2025.acl-tutorials)

Challenge:	Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance.
Approach:	They propose to use AMR to map meanings of 1,685 utterances to 50+ languages to build a dataset 20 times larger than existing resources.
Outcome:	The proposed dataset covers more languages, has more utterances, and has localized or translated entities for each language.

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Artem Shelmanov, Maxim Panov, Roman Vashurin, Artem Vazhentsev, Ekaterina Fadeeva, Timothy Baldwin

Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)

Challenge:	Large language models (LLMs) produce hallucinations, which undermine user trust and reliability.
Approach:	This tutorial offers the first systematic introduction to uncertainty quantification (UQ) for LLMs in text generation tasks.
Outcome:	The proposed framework provides tools for communicating the reliability of a model answer.

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Xinping Zhao, Jindi Yu, Zhenyu Liu, Jifang Wang, Dongfang Li, Yibin Chen, Baotian Hu, Min Zhang

HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs (2024.eacl-srw)

Challenge:	Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know".
Approach:	They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content.
Outcome:	The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content.

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Cem Uluoglakci, Tugba Temizel

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

Challenge:	Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications.
Approach:	They propose a framework that combines benchmarking LLMs’ hallucination tendencies with efficient hallucinian detection.
Outcome:	The proposed framework provides opportunities to test and improve LLMs and can generate benchmarking datasets tailored to specific domains.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels (2026.eacl-srw)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Hikari Tanaka, Atsushi Keyaki, Mamoru Komachi

Text Generation

Challenge:	Large language models (LLMs) can generate fluent text, but the quality of generated content depends on its consistency with the given input.
Approach:	They constructed a Japanese evaluation dataset for hallucination detection in summarization by manually annotating sentence-level faithfulness labels in LLM-generated summaries of Japanese documents.
Outcome:	The proposed model can detect hallucinations in Japanese documents by annotating faithfulness labels in Japanese summaries.

ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination Detection (2024.acl-srw)

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Janek Herrlein, Chia-Chien Hung, Goran Glava�

Handling Ontology Gaps in Semantic Parsing (2024.starsem-1)

Challenge:	ANHALTEN is a new evaluation dataset that extends the English hallucination detection dataset to German.
Approach:	They propose a dataset that extends the English hallucination detection dataset to German . they show that larger context length leads to better halluciation detection in german .
Outcome:	ANHALTEN is the first evaluation dataset that extends the English hallucination detection dataset to German.

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Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti

Semantic Text Processing

Challenge:	Existing methods to detect hallucinations in closed-ontology models are limited by ontology gaps.
Approach:	They propose a framework for stimulating and analyzing NSP model hallucinations . they propose 'hallucination simulation framework' to detect hallucinosities in presence of ontology gaps .
Outcome:	The proposed framework improves the F1-Score and the IQ Pro benchmark datasets.

Luna: A Lightweight Evaluation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost (2025.coling-industry)

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Masha Belyi, Robert Friel, Shuai Shao, Atindriyo Sanyal

Enhancing Hallucination Detection via Future Context (2026.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) systems are crucial for enhancing the capabilities of large language models (LLMs) in industry applications.
Approach:	They propose a DeBERTA-large encoder for hallucination detection in RAG settings that is fine-tuned for halluination detection.
Outcome:	The proposed model outperforms GPT-3.5 and commercial evaluation frameworks on the hallucination detection task, with 97% and 91% reduction in cost and latency, respectively.

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Joosung Lee, Cheonbok Park, Hwiyeol Jo, Jeonghoon Kim, Joonsuk Park, Kang Min Yoo

Diving Deep into Modes of Fact Hallucinations in Dialogue Systems (2022.findings-emnlp)

Challenge:	Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process.
Approach:	They propose a framework for detection of hallucinations in black-box generators by analyzing future contexts.
Outcome:	The proposed framework improves on existing methods and demonstrates that it is feasible to integrate it with other models.

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Souvik Das, Sougata Saha, Rohini Srihari

Natural Language Interfaces

Challenge:	Knowledge Graph(KG) grounded conversations often use large pre-trained models and suffer from fact hallucination.
Approach:	They propose to use a human feedback analysis to identify various modes of hallucination in KG chatbots.
Outcome:	The proposed system provides fine-grained signals that control fallacious content while generating responses.

An Automatic Method to Estimate Correctness of RAG (2025.coling-industry)

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Chi Zhang, Vivek V. Datla, Aditya Shrivastava, Alfy Samuel, Zhiqi Huang, Anoop Kumar, Daben Liu

Challenge:	Existing methods to assess the correctness of RAG models fail to capture the model’s internal state during answer generation.
Approach:	They propose a method to predict the correctness of RAG models by modeling the model’s uncertainty on quantified perturbations of input.
Outcome:	Extensive experiments across multiple large language models show that the proposed approach quantifies RAG robustness by aligning predictions with ground truth with a MSE 0.002 while offering flexibility for diverse qualitative metrics.

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)

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Jiahao Cheng, Tiancheng Su, Jia Yuan, Guoxiu He, Jiawei Liu, Xinqi Tao, Jingwen Xie, Huaxia Li

Logic Matters in Lightweight Hallucination Classification for RAG System (2026.acl-long)

Challenge:	Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored.
Approach:	They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods.
Outcome:	The proposed method significantly affects the internal states and token probability distributions of the LLM.

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Ningyuan Yang, Kaizhu Huang

Information Retrieval

Challenge:	Existing hallucination detection frameworks for RAGs lack robustness and performance . a compact model may lose track of precise information in retrieved segments or misinterpret a document's entailment score.
Approach:	They propose a lightweight, modular framework for hallucination detection in RAG systems . they capture logical relationships among retrieved documents within the vector space .
Outcome:	The proposed framework improves hallucination detection in RAG systems without complex architectures or pre-training on datasets.

Citation-Enhanced Generation for LLM-based Chatbots (2024.acl-long)

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Weitao Li, Junkai Li, Weizhi Ma, Yang Liu

Natural Language Interfaces

Challenge:	Existing efforts to alleviate hallucination in chatbots require additional training and data annotation.
Approach:	They propose a Citation-Enhanced Generation approach that uses retrieval argumentation to generate citations and a natural language inference-based citation generation module to generate content.
Outcome:	The proposed method outperforms state-of-the-art methods on three benchmarks.

Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation (2025.acl-short)

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Chengwei Qin, Wenxuan Zhou, Karthik Abinav Sankararaman, Nanshu Wang, Tengyu Xu, Alexander Radovic, Eryk Helenowski, Arya Talebzadeh, Aditya Tayade, Sinong Wang, Shafiq Joty, Han Fang, Hao Ma

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence (2026.acl-long)

Challenge:	Existing methods for detecting hallucination in long-form tasks focus on limited domains or rely heavily on external fact-checking tools, which may not always be available.
Approach:	They propose a new paradigm that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection.
Outcome:	The proposed method outperforms existing methods for detecting hallucination in open-domain long-form generation and is more accurate than random guessing.

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Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

HalluZig: Hallucination Detection using Zigzag Persistence (2026.eacl-long)

Challenge:	Existing methods for standard generation tasks fail to capture the unique dynamics of ICL.
Approach:	They propose a concept of self-function vectors that leverage Bayesian views and the mechanistic interpretability of ICL to model latent concept learned during in-context prompting.
Outcome:	The proposed framework can be used for trustworthy-related applications, such as hallucination detection.

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Shreyas N. Samaga, Gilberto Gonzalez Arroyo, Tamal K. Dey

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

Challenge:	Existing methods for hallucination detection rely on surface-level signals from the model output, overlooking the failures within the model’s internal reasoning process.
Approach:	They propose a framework that analyzes the dynamic topology of the evolution of model’s layer-wise attention and leverage zigzag persistence to extract a topological signature.
Outcome:	The proposed framework outperforms baselines on multiple benchmarks and is generalizable across models.

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models (2024.lrec-main)

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

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Zican Dong, Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen

Reasoning’s Razor: Reasoning Improves Accuracy but Hurts Recall at Critical Operating Points in Safety and Hallucination Detection (2026.eacl-long)

Challenge:	Existing long context models suffer from performance decline when the input text exceeds their length limit.
Approach:	They propose a multi-task long context benchmark to evaluate LLMs' long context ability using 10 datasets from 5 different NLP tasks.
Outcome:	The proposed model covers 5 domains and core capacities of large language models.

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Atoosa Chegini, Hamid Kazemi, Garrett Souza, Maria Safi, Yang Song, Samy Bengio, Sinead Williamson, Mehrdad Farajtabar

Long-form Hallucination Detection with Self-elicitation (2025.findings-acl)

Challenge:	a new study examines the suitability of reasoning for precision-sensitive classification tasks . false positives carry severe operational consequences, such as blocking legitimate queries .
Approach:	They propose to use reasoning for classification tasks under low false positive rate regimes . they find that Think On improves overall accuracy, but performs poorly at low FPRs a .
Outcome:	The proposed reasoning-augmented generation model outperforms self-verbalized confidence in precision-sensitive deployments.

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Zihang Liu, Jiawei Guo, Hao Zhang, Hongyang Chen, Jiajun Bu, Haishuai Wang

A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection (2023.findings-emnlp)

Challenge:	Existing methods for hallucination detection tend to decompose text into isolated statements, unable to understand contextual semantics.
Approach:	They propose a framework to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics.
Outcome:	The proposed framework enables self-elicitation to elicit expressions of knowledge and understand semantics.

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Shiping Yang, Renliang Sun, Xiaojun Wan

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments (2026.acl-long)

Challenge:	Large Language Models (LLMs) are capable of working with humans in real-world scenarios, but they are prone to generate hallucinations and misinformation when deployed for mission-critical tasks.
Approach:	They propose a self-check approach to detect factual errors in a zero-resource fashion by using reverse validation to generate a hallucination detection benchmark.
Outcome:	The proposed method outperforms baseline methods while costing fewer tokens and less time.

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Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang, Juan Cao

Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity (2026.eacl-long)

Challenge:	Existing methods for hallucination detection focus on implicit neural uncertainty or explicit symbolic reasoning, ignoring factual hallucinosities.
Approach:	They propose a framework that bridges neural features and symbolic judgments for hallucination detection by leveraging a "meta-judgment" process to map symbolic labels back into the feature space.
Outcome:	Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB.

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Prakhar Ganesh, Reza Shokri, Golnoosh Farnadi

Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages (2026.findings-eacl)

Challenge:	Existing hallucination evaluations focus only on correctness and often overlook consistency . a significant inconsistency in benchmarks like Med-HALT suggests hallucianation-related harms have been misunderstood.
Approach:	They propose a framework for quantifying consistency in hallucination evaluations . they find that detection techniques detect consistency, not correctness .
Outcome:	The proposed framework uncovers critical limitations in hallucination evaluations.

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Sello Ralethe, Jan Buys

Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Large Language Models generate false or unsupported information, which can be difficult to detect in low-resource languages.
Approach:	They propose a cross-lingual benchmark for hallucination detection spanning English and South African languages.
Outcome:	The proposed model detects 23.6% fewer hallucinations in South African languages compared to English . human validation confirms the quality and cross-lingual alignment of the model .

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Ekaterina Fadeeva, Aleksandr Rubashevskii, Dzianis Piatrashyn, Roman Vashurin, Shehzaad Dhuliawala, Artem Shelmanov, Timothy Baldwin, Preslav Nakov, Mrinmaya Sachan, Maxim Panov

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

Challenge:	Existing approaches to mitigating hallucinations conflate factuality with faithfulness to the retrieved evidence, incorrectly labeling factually correct statements as hallucinos . Existing methods to mitigate hallucinics rely on a lack of training data coverage, input ambiguity, and architectural constraints.
Approach:	They propose a method for hallucination detection in Large Language Models enhanced with knowledge retrieval based on faithfulness to the retrieved context.
Outcome:	The proposed method outperforms unsupervised UQ baselines, RAG-specific methods, and supervised classifiers across multiple tasks and LLMs.

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

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Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li

Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs (2025.naacl-long)

Challenge:	Current approaches typically merge sentence-level parsing outputs for discourse input, resulting in fragmented graphs and degraded downstream performance.
Approach:	They propose a task for discourse-level text scene graph parsing that merges sentence-level outputs for discourse input and propose 'DiscoSG' a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs is used to test the new task.
Outcome:	The proposed task improves SPICE by 30% over the baseline while achieving 86 faster inference than existing models.

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Anirudh Phukan, Divyansh Divyansh, Harshit Kumar Morj, Vaishnavi Vaishnavi, Apoorv Saxena, Koustava Goswami

DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)

Challenge:	Large Multimodal Models are plagued by hallucinations that limit their reliability and adoption.
Approach:	They propose a method that leverages contextual token embeddings from LMMs to detect hallucinations.
Outcome:	The proposed method improves hallucination detection and grounding across diverse categories while excelling in tasks requiring contextual understanding.

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Kedi Chen, Qin Chen, Jie Zhou, He Yishen, Liang He

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks for hallucination detection are intentionally generated by large language models (LLMs) however, many focus on factuality while ignoring faithfulness.
Approach:	They propose a dialogue-level hallucination evaluation benchmark for large language models . they integrate the topic into prompts and facilitate a dialog between two LLMs .
Outcome:	The proposed benchmark covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucines.

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)

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Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty (2025.findings-emnlp)

Challenge:	a growing number of researchers are studying the hallucination issue in large language models.
Approach:	They propose a hallucination detection benchmark and a method to detect hallucines in LLMs.
Outcome:	The proposed method detects hallucinations and mitigates them using different training stages.

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Yu Feng, Phu Mon Htut, Zheng Qi, Wei Xiao, Manuel Mager, Nikolaos Pappas, Kishaloy Halder, Yang Li, Yassine Benajiba, Dan Roth

Challenge:	Existing methods to gauge model’s uncertainty through self-consistency in responses to the target query are misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same query when answering a knowledge-preserving perturbation of the query.
Approach:	They propose a method that uses multi-agent interaction to estimate black-box LLMs' uncertainty.
Outcome:	The proposed method outperforms existing self-consistency based methods and improves hallucination detection.

Beyond Output Confidence: Epistemic-Aware Hallucination Detection with Answer-Level Signals (2026.findings-acl)

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Jieran Li, Xiuyuan Hu, Yang Zhao, Dongbiao Sun, Hao Zhang

German Also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset (2024.lrec-main)

Challenge:	Existing methods for detecting hallucinations are confounded by epistemic uncertainty and cannot distinguish genuine uncertainty from fabricated content.
Approach:	They propose a model-agnostic metric that captures epistemic boundary deviations by measuring answer-level stability across multiple stochastic forward passes.
Outcome:	The proposed metric outperforms strong uncertainty-only baselines and can be used to detect hallucinations on open-domain question answering, dialogue generation, and code completion.

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Laura Mascarell, Ribin Chalumattu, Annette Rios

Information Extraction & Text Mining

Challenge:	Large Language Models (LLMs) have made significant progress on a wide range of natural language processing tasks, but they still suffer from hallucinating information in their output.
Approach:	They propose to use an annotated dataset to detect hallucinations in german news summarization and open-source it to foster further research on hallucinosity detection in german.
Outcome:	The proposed model can detect hallucinations in the output and evaluate the faithfulness of the summaries.

Rethinking Evaluation for LLM Hallucination Detection: A Desiderata, A New RAG-based Benchmark, New Insights (2026.acl-long)

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Wenbo Chen, Veena Padmanabhan, Tootiya Giyahchi, Elaine Wong, Leman Akoglu

The Impact of Negated Text on Hallucination with Large Language Models (2025.emnlp-main)

Challenge:	Existing benchmarks lack long context and label noise for stress-testing detectors . a new RAG-based HDB that underwent a rigorous human annotation process is developed .
Approach:	They propose a desiderata of properties for hallucination detection benchmarks to exhibit . they build a RAG-based HDB that underwent a rigorous human annotation process .
Outcome:	The proposed benchmark exhibits all desirable properties of existing HDBs . existing benchmarks lack realistic label noise for stress-testing detectors despite human annotation .

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Jaehyung Seo, Hyeonseok Moon, Heuiseok Lim

HAT: Hallucination Annotation for Translation (2026.acl-long)

Challenge:	Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing.
Approach:	They propose to examine whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases.
Outcome:	The proposed model can detect hallucinations comparable to affirmative cases, but it is difficult to detect them in negated text, the authors show .

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Rajen Chatterjee, Xintong Li, Paisarn Charoenpornsawat, Allen Lee

Machine Translation

Challenge:	Hallucinations in machine translation (MT) outputs are prone to hallucination, authors say . lack of high-quality benchmarks for halluciation detection has hindered MT deployments .
Approach:	They propose a dataset that provides annotated hallucination distributions and benchmarks . they use 350,959 span-level annotations across 38 language pairs to analyze hallucis a MT output .
Outcome:	The proposed dataset provides high-quality benchmarks for hallucination detection in machine translation . the dataset includes 350,959 span-level annotated samples across 38 language pairs .

Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination (2024.emnlp-main)

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Pittawat Taveekitworachai, Febri Abdullah, Ruck Thawonmas

InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States (2024.findings-emnlp)

Challenge:	a series of investigations into an interesting phenomenon where performance increases in large language models when providing a prompt that causes and exploits hallucination.
Approach:	They propose a null-shot prompting approach that intentionally instructs LLMs to look at and utilize information from a nil section.
Outcome:	The proposed approach causes and exploits hallucination in large language models on a range of tasks including arithmetic reasoning, commonsense reasoning, and reading comprehension.

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Mohammad Beigi, Ying Shen, Runing Yang, Zihao Lin, Qifan Wang, Ankith Mohan, Jianfeng He, Ming Jin, Chang-Tien Lu, Lifu Huang

SkillAggregation: Reference-free LLM-Dependent Aggregation (2025.acl-long)

Challenge:	Large Language Models (LLMs) often struggle with generating reliable outputs, often producing high-confidence inaccuracies known as hallucinations.
Approach:	They propose a framework that leverages contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers to enhance confidence estimation in LLMs.
Outcome:	The framework outperforms existing methods in the hallucination detection benchmark HaluEval and the previous methods at the same time.

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Guangzhi Sun, Anmol Kagrecha, Potsawee Manakul, Phil Woodland, Mark Gales

Challenge:	Existing methods in NLP assign equal weight to all LLM judgments or are designed for specific tasks such as hallucination detection.
Approach:	They propose a method that learns to combine LLM judgments without additional data or ground truth to exploit the judge estimates during inference.
Outcome:	The proposed method outperforms Crowdlayer on all tasks and yields the best performance over all approaches on the majority of tasks.

Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection (2023.findings-emnlp)

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Kishan Maharaj, Ashita Saxena, Raja Kumar, Abhijit Mishra, Pushpak Bhattacharyya

Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)

Challenge:	Existing methods for hallucination detection depend on knowledge sources that are explicit such as Wikipedia or knowledge graphs.
Approach:	They propose a cognitive approach that leverages gaze signals from humans to detect hallucinations in natural language processing (NLP) they collect and introduce an eye tracking corpus consisting of 500 instances, annotated by five annotators for hallucinism detection.
Outcome:	The proposed approach achieves a balanced accuracy of 87.1% on a FactCC dataset.

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Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida, André Martins

Multilinguality

Challenge:	Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust.
Approach:	They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model.
Outcome:	The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples.

VADE: Visual Attention Guided Hallucination Detection and Elimination (2025.findings-acl)

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Vishnu Prabhakaran, Purav Aggarwal, Vinay Kumar Verma, Gokul Swamy, Anoop Saladi

Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector (2024.emnlp-main)

Challenge:	Vision Language Models (VLMs) are prone to hallucinations, generating outputs that lack grounding in the actual visual data.
Approach:	They propose a sequence modelling approach to learn complex sequential patterns from transformer attention maps.
Outcome:	The proposed approach achieves an average PR-AUC of 80% in hallucination detection on M-HalDetect and an 5% improvement in hallucinosis mitigation on MSCOCO.

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Xiaoxue Cheng, Junyi Li, Xin Zhao, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Kun Gai, Ji-Rong Wen

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models (2024.findings-acl)

Challenge:	Existing studies on hallucination detection rely heavily on closed-source LLMs such as GPT-4.
Approach:	They propose an LLM-based agent framework called HaluAgent that integrates LLMs, multi-functional toolbox and a memory mechanism for hallucination detection.
Outcome:	The proposed framework integrates the LLM, multi-functional toolbox, and can detect hallucinations on Chinese and English datasets.

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Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, Yiqun Liu

Hallucination Detection in Structured Query Generation via LLM Self-Debating (2025.findings-emnlp)

Challenge:	Existing studies on hallucination detection for LLMs focus on how to identify possible factrelated errors in outputs.
Approach:	They propose an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations.
Outcome:	The proposed framework outperforms existing state-of-the-art methods in hallucination detection.

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Miaoran Li, Jiangning Chen, Minghua Xu, Xiaolong Wang

Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations (2026.acl-long)

Challenge:	Hallucination remains a key challenge in applying large language models to structured query generation . we propose the Self-Debating framework to enhance detection performance .
Approach:	They propose a framework that prompts an LLM to generate contrastive explanations from opposing perspectives . they also propose 'self-debating' framework to enhance detection performance .
Outcome:	The proposed framework outperforms LLM-as-a-Judge baselines in hallucination detection . the framework generates contrastive explanations from opposing perspectives .

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Shanghao Li, Jinda Han, Yibo Wang, Yuanjie Zhu, Zihe Song, Langzhou He, Kenan Kamel A Alghythee, Philip S. Yu

Semantic Text Processing

Challenge:	Existing literature primarily addresses this problem through external interventions such as retrieval augmentation and prompt engineering at the input or output level.
Approach:	They find that LLMs can still produce hallucinated outputs when using structured external knowledge.
Outcome:	The proposed models fail to ground the provided knowledge, causing the model to revert to parametric memory.

Hallucination Detection for Generative Large Language Models by Bayesian Sequential Estimation (2023.emnlp-main)

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Xiaohua Wang, Yuliang Yan, Longtao Huang, Xiaoqing Zheng, Xuanjing Huang

Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution (2025.findings-emnlp)

Challenge:	Existing methods for detecting hallucinations require large numbers of observations to be retrieved, increasing response times.
Approach:	They propose a framework that leverages Bayesian sequential analysis to optimize the trade-off between costs and benefits during the hallucination detection process.
Outcome:	The proposed framework surpasses existing methods in efficiency and precision of hallucination detection.

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Zuzanna Dubanowska, Maciej Żelaszczyk, Michał Brzozowski, Paolo Mandica, Michal P. Karpowicz

The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)

Challenge:	a spurious correlation between hallucination detection methods and data is limiting the current SOTA.
Approach:	They propose a set of guidelines for hallucination detection and its evaluation.
Outcome:	The proposed method performs no better than supervised linear probes on the RAGTruth dataset .

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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu

Prompt-Guided Internal States for Hallucination Detection of Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations.
Approach:	They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions.
Outcome:	The proposed framework outperforms white-box methods and reduces computational overhead by over 90%.

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Fujie Zhang, Peiqi Yu, Biao Yi, Baolei Zhang, Tong Li, Zheli Liu

VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck (2026.acl-long)

Challenge:	Large Language Models (LLMs) generate incorrect or logically incorrect responses, which is known as LLM hallucinations.
Approach:	They propose a framework for supervised hallucination detection using in-domain data by prompting changes to the structure related to text truthfulness in LLMs’ internal states.
Outcome:	The proposed framework enhances the cross-domain generalization of existing hallucination detection methods.

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Feiran Zhang, Yixin Wu, Zhenghua Wang, Xiaohua Wang, Changze Lv, Xuanjing Huang, Xiaoqing Zheng

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs (2025.findings-emnlp)

Challenge:	Existing hallucination detection methods rely on external verification tools . however, entanglement of visual-linguistic syntax and noise makes it difficult to detect hallucis .
Approach:	They propose a hallucination detection framework that leverages the Variational Information Bottleneck theory to detect hallucinic heads and to infer hallucication mitigation strategies.
Outcome:	The proposed framework outperforms baselines in hallucinations and noise detection environments.

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Yao Fu, Runchao Li, Xianxuan Long, Haotian Yu, Xiaotian Han, Yu Yin, Pan Li

Challenge:	Neural network pruning disrupts LLMs’ internal activation features crucial for lie detection . layer-wise pruning sparsity inadvertently removes crucial weights, failing to improve lie detection performance despite its reliance on the most crucial LLM layer.
Approach:	They propose a pruning approach that places greater emphasis on layers with more activation outliers and stronger discriminative features simultaneously.
Outcome:	The proposed approach improves the hallucination detection for pruned LLMs (achieving 88% accuracy at 50% sparsity) and enhances their performance on TruthfulQA.

TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG (2026.acl-long)

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Pengqian Lu, Jie Lu, Anjin Liu, Guangquan Zhang

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)

Challenge:	Existing approaches attribute hallucinations to a binary conflict between internal knowledge stored in FFNs and the retrieved context.
Approach:	They propose a framework which mathematically attributes each next-token probability to seven distinct sources and aggregates source attributions by POS tags to quantify contribution of each model component to the generation of specific linguistic categories within a response.
Outcome:	Extensive experiments show that the proposed framework achieves state-of-the-art performance.

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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang

Hallucination Detection in LLMs Using Spectral Features of Attention Maps (2025.emnlp-main)

Challenge:	Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear.
Approach:	They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer .
Outcome:	The proposed applications improve hallucination detection performance by integrating two different inputs.

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Jakub Binkowski, Denis Janiak, Albert Sawczyn, Bogdan Gabrys, Tomasz Jan Kajdanowicz

Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable performance across tasks but remain prone to hallucinations.
Approach:	They propose a method that uses attention maps to detect hallucinations . they propose to use top-k eigenvalues of the attention maps as input to probes .
Outcome:	The proposed method achieves state-of-the-art hallucination detection performance among attention-based methods.

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Jianpeng Hu, Yanzeng Li, Jialun Zhong, Lei Zou, Wenfa Qi

Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Existing methods for detecting faithfulness hallucinations are coarse or do not capture the models’ internal reasoning processes, making it difficult to learn.
Approach:	They propose a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination using Large language models.
Outcome:	The proposed method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.

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Runsong Jia, Zhen Fang, Mengjia Wu, Jie Lu, Yi Zhang

ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries (2025.acl-long)

Challenge:	Existing methods for hallucination detection depend on internal signals like uncertainty and self-consistency checks to identify unreliable outputs.
Approach:	They propose a retrieval-augmented generation method to enhance hallucination detection by addressing information updating challenges.
Outcome:	The proposed method improves on existing methods with strong generalization capabilities.

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Kishan Maharaj, Vitobha Munigala, Srikanth G. Tamilselvam, Prince Kumar, Sayandeep Sen, Palani Kodeswaran, Abhijit Mishra, Pushpak Bhattacharyya

LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation (2026.acl-long)

Challenge:	Recent advances in large language models have significantly enhanced their ability to understand both natural language and code, but are prone to hallucinations.
Approach:	They propose a first-of-its-kind dataset, CodeSumEval, with 10K samples, curated specifically for hallucination detection in code summarisation.
Outcome:	The proposed framework has a 73% F1 score and is curated specifically for detection of hallucinations in code summarisation.

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Caiqi Zhang, Xiaochen Zhu, Chengzu Li, Nigel Collier, Andreas Vlachos

VISTA: Verification In Sequential Turn-based Assessment (2026.acl-long)

Challenge:	Existing methods for hallucination detection are limited to short-form question answering tasks and do not generalize well to open-ended generation.
Approach:	They propose a method that trains LLMs to append a numerical confidence score to each generated statement during long-form generation.
Outcome:	The proposed method is 20 faster than traditional self-consistency methods while achieving better calibration.

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Ashley Lewis, Andrew Perrault, Eric Fosler-Lussier, Michael White

Dialogue Systems & Conversational Agents

Challenge:	Existing metrics evaluate isolated responses or treat unverifiable content as errors, limiting their use for multi-turn dialogue.
Approach:	They propose a framework for evaluating conversational factuality via claim-level verification and sequential consistency tracking.
Outcome:	The proposed framework improves hallucination detection over existing benchmarks and models.

PROBE: PROcess-Based BEnchmark for Hallucination Detection (2026.findings-acl)

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Yu Zhang, Peter Belcak, Shizhe Diao, Yonggan Fu, Shaona Ghosh, Morteza Mardani, Eileen Margaret Peters Long, Bei Yu, Pavlo Molchanov

Efficient Hallucination Detection in Automatic Code Generation (2026.findings-acl)

Challenge:	Existing agentic applications rely on LLMs to self-assess the factuality of outputs . but current LLM systems fail to detect hallucinations .
Approach:	They propose a benchmark that breaks down hallucination detection into four critical steps . they show that when halluciation detection is treated as a multi-step process, all models achieve considerably better performance.
Outcome:	The proposed benchmark breaks down hallucination detection into four critical steps . it shows that when halluciation detection is treated as a multi-step process, all models achieve considerably better performance.

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Georgii Andriushchenko, Roman Garaev, Lyudmila Rvanova, Artem Shelmanov, Vladimir V. Ivanov

Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps (2026.findings-acl)

Challenge:	Large language models produce source code that appears correct and well-formed, but includes hallucinated elements that cause downstream test failures.
Approach:	They develop a transformer-based detector that uses LLM internal representations to identify hallucinations.
Outcome:	The proposed detector outperforms existing methods and unsupervised methods in the code generation domain.

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Jonas Waldendorf, Bashar Awwad Shiekh Hasan, Evgenii Tsymbalov