Papers with detection methods
SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System (2026.eacl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promise in identifying deception, but their cognitive assistance potential remains underexplored. |
| Approach: | They propose a framework for LLM-based scam detection that bridges automated reasoning and human cognition. |
| Outcome: | The proposed framework outperforms GPT-4o in the Korean scam detection and phone scam simulations. |
MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)
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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang
| Challenge: | Existing research has focused on evaluating detection methods for specific domains or language models. |
| Approach: | They build a testbed to detect texts from diverse human writings and LLMs using different detection methods. |
| Outcome: | Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios. |
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking (2024.acl-demos)
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| Challenge: | MGTD methods are needed in many areas, such as prevention of disinformation spreading, plagiarism, impersonation and identity theft. |
| Approach: | They propose a framework for machine-generated text detection that integrates custom methods and evaluation datasets into existing frameworks. |
| Outcome: | The proposed framework simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets. |
Do LLMs Really Know What They Don’t Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness (2026.findings-acl)
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| Challenge: | Recent work suggests that large language models (LLMs) produce hallucinated and factually correct outputs. |
| Approach: | They propose a taxonomy categorizing hallucinations into Unassociated Hallucination (UH) and Associated Hallucinian (AH) they propose to use internal signals to distinguish hallucinos from factual errors . |
| Outcome: | The proposed taxonomy categorizes hallucinations into Unassociated Hallucination (UH) and Associated Hallucinications (AHs) based on the proposed taxonomic, the authors show that hidden states reflect whether the model is recalling parametric knowledge rather than the truthfulness of the output itself. |
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts (2025.acl-long)
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| Challenge: | Existing methods for detecting social-media texts are limited to the English language and longer texts are not easily recognisable by humans. |
| Approach: | They propose to use a multilingual and multi-platform dataset to compare machine-generated text detection methods in the social-media domain to compare them to human-written texts. |
| Outcome: | The proposed dataset contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual LLMs. |
GameTox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities (2025.naacl-short)
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| Challenge: | Existing methods to detect toxic behavior in online gaming environments are limited by utterance-level annotation. |
| Approach: | They propose to annotate game chat utterances for toxicity detection through intent classification and slot filling. |
| Outcome: | The proposed model improves the detection of toxic speech in online gaming environments and reveals limitations of current models. |
CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)
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| Challenge: | Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection. |
| Approach: | They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information. |
| Outcome: | The proposed method improves on three text classification tasks on four advanced attack algorithms. |
Utterance-level Detection Framework for LLM-Involved Content Detection in Conversational Setting (2026.eacl-long)
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| Challenge: | Existing methods focus on static, document-level content, overlooking the dynamic nature of dialogues. |
| Approach: | They propose an utterance-level detection framework which integrates features from individual and combined analysis of dialogue participants’ responses to detect LLM-generated text under conversational setting. |
| Outcome: | The proposed framework achieves 98.14% accuracy with high inference speed and extensive results on different models and settings. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| 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. |
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels, but their effective integration is non-trivial. |
| Approach: | They propose a global label propagation network with LLM-based pseudo labels for multimodal fake news detection which integrates LLM capabilities via label propagations. |
| Outcome: | The proposed model outperforms state-of-the-art models on benchmark datasets showing that it can propagate pseudo labels among all samples. |
Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) is becoming more accurate, but hallucinations are extremely pathological . previous work focused on artificial settings where the problem is amplified, disregarding some common types of hallucines . |
| Approach: | They propose a method for alleviating hallucinations at test time that significantly reduces the hallucinic rate. |
| Outcome: | The proposed method significantly reduces the hallucinatory rate in a natural setting. |
SearchLLM: Detecting LLM Paraphrased Text by Measuring the Similarity with Regeneration of the Candidate Source via Search Engine (2026.eacl-long)
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| Challenge: | Large language models (LLMs) can be used to enhance text quality but can sometimes result in loss or distortion of original meaning. |
| Approach: | They propose a method to identify LLM-paraphrased text by leveraging search engine capabilities to locate potential original text sources. |
| Outcome: | The proposed approach distinguishes LLM-paraphrased text from genuine human writing . it uses search engine capabilities to integrate with existing detectors to improve performance . |
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
| Approach: | They propose a framework for multimodal deepfake detection that uses contrastive learning and large language models to mitigate modality fragmentation and refine embeddings to address shallow inter-modal reasoning. |
| Outcome: | ConLLM reduces audio deepfake EER by 50%, improves video accuracy by 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. |
DA3: A Distribution-Aware Adversarial Attack against Language Models (2024.emnlp-main)
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| Challenge: | Recent attacks have shown that adversarial examples have a different data distribution than the original examples, reducing their effectiveness under detection methods. |
| Approach: | They propose a distribution-aware adversarial attack method that considers the distribution shifts of adversarials to improve attacks’ effectiveness under detection methods. |
| Outcome: | The proposed method improves the effectiveness of adversarial examples under detection methods and integrates both ASR and detectability. |
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors (2025.findings-acl)
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Andrea Pedrotti, Michele Papucci, Cristiano Ciaccio, Alessio Miaschi, Giovanni Puccetti, Felice Dell’Orletta, Andrea Esuli
| Challenge: | Recent advances in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. |
| Approach: | They evaluate the resilience of state-of-the-art MGT detectors to linguistically informed adversarial attacks by using Direct Preference Optimization to shift the MGT style toward human-written text. |
| Outcome: | The proposed pipeline fine-tunes language models to shift the MGT style toward human-written text (HWT) it obtains generations more challenging to detect by current models, and shows that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detecting performances. |
HalluZig: Hallucination Detection using Zigzag Persistence (2026.eacl-long)
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| 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. |
Non-Existent Relationship: Fact-Aware Multi-Level Machine-Generated Text Detection (2025.emnlp-main)
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| Challenge: | Existing methods for detecting LLMs lack the authenticity of the entity graph . lmgenerated text is misused, including fake news and spam . |
| Approach: | They propose a fact-aware model that assesses discrepancies between textual and factual entity graphs through graph comparison. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets showing that it can capture differences in entity graphs between machine-generated and human-written texts. |
GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization (2026.findings-acl)
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Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Aleksandr Gordeev, Vladimir Dokholyan, Maksim Kuprashevich
| Challenge: | GigaCheck is a framework for AI-generated text detection. |
| Approach: | They propose a dual-strategy framework for AI-generated text detection . they leverage representation learning of fine-tuned LLMs to discern authorship . |
| Outcome: | The proposed framework can detect LLM-generated content with high accuracy and accuracy . it can be used in mixed-authorship scenarios and in academic collaborations . |
MATCHED: Multimodal Authorship-Attribution To Combat Human Trafficking in Escort-Advertisement Data (2025.findings-acl)
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| Challenge: | Existing methods for human trafficking detection ignore the multimodal nature of online ads . sex trafficking is a pervasive crime exploiting individuals of all ages and genders . |
| Approach: | They propose to use multimodal authorship attributes to identify suspicious ads that combine text and images to improve vendor identification and verification tasks. |
| Outcome: | The proposed model outperforms existing methods for vendor identification and verification tasks using text-only, vision-only and multimodal training objectives. |
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. |
HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token (2026.eacl-long)
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| Challenge: | Existing methods for detection of hallucinations operate after text generation, making intervention costly and untimely. |
| Approach: | They examine whether hallucination risk can instead be predicted before any token is generated by probing a model's internal representations in a single forward pass. |
| Outcome: | The proposed model can detect hallucinations before token generation, while query-token representations can be more accurate. |
A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection (2025.emnlp-main)
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| Challenge: | Existing methods for misinformation detection are limited by domain knowledge and expert experience. |
| Approach: | They propose a Multi-Agent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO) they first employ multiple expert agents to analyze target-domain news, then introduce a question-reflection mechanism that guides expert agents for higher-quality analysis. |
| Outcome: | The proposed framework improves on a common dataset and shows that iteratively improves over existing methods. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
Learning to Rewrite: Generalized LLM-Generated Text Detection (2025.acl-long)
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| Challenge: | Existing detectors for Large Language Models (LLMs) struggle to generalize in open-world settings. |
| Approach: | They propose a framework to detect LLM-generated text with exceptional generalization to unseen domains by reinforcing LLMs’ inherent rewriting tendencies. |
| Outcome: | The proposed framework outperforms state-of-the-art detection methods by 23.04% in AUROC, 35.10% for out-of distribution tests, and 48.66% under adversarial attacks. |
Where Do People Tell Stories Online? Story Detection Across Online Communities (2024.acl-long)
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| Challenge: | Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. |
| Approach: | They propose a toolkit to detect stories in online communities using an annotated reddit dataset and a codebook adapted to social media context. |
| Outcome: | The proposed toolkit includes an annotation-rich dataset of 502 Reddit posts and comments . it also includes a codebook adapted to the social media context and models to predict storytelling at document and span levels. |
MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention (2024.acl-long)
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| Challenge: | Existing studies on content moderation of toxic memes focus on text-based content . current research neglects the widespread influence of multimodal content like memes . |
| Approach: | They propose a framework leveraging Large Language Models and Visual Language Model (VLMs) for meme intervention. |
| Outcome: | The proposed framework enables users to generate relevant and effective responses to toxic memes. |
HVGuard: Utilizing Multimodal Large Language Models for Hateful Video Detection (2025.emnlp-main)
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Yiheng Jing, Mingming Zhang, Yong Zhuang, Jiacheng Guo, Juan Wang, Xiaoyang Xu, Wenzhe Yi, Keyan Guo, Hongxin Hu
| Challenge: | Existing methods for hateful video detection rely on unimodal analysis or feature fusion . Existing tools struggle to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor . |
| Approach: | They propose a reasoning-based hateful video detection framework with multimodal large language models . they integrate Chain-of-Thought reasoning to enhance multimodal interaction modeling . |
| Outcome: | The proposed framework outperforms existing tools on two public datasets covering English and Chinese. |
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)
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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
| Challenge: | Large language models (LLMs) are capable of performing tasks but are likely to be misused. |
| Approach: | They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model . |
| Outcome: | The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts . |
PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025.findings-naacl)
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| Challenge: | Recent methods for detecting LLM-generated text have shown impressive performance, but in real-world scenarios, users often introduce perturbations to the text. |
| Approach: | They propose a method that detects syntactic trees that are minimally affected by perturbations and exhibit distinct differences between human-written and LLM-generated text. |
| Outcome: | The proposed method shows that it is significantly better against perturbations on the HC3 and GPT-3.5-mixed datasets and also has the shortest time expenditure. |
Enhancing LLM Text Detection with Retrieved Contexts and Logits Distribution Consistency (2025.emnlp-main)
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| Challenge: | Existing methods for detecting large language models (LLMs) generate fluent text, but they only use a few tokens due to the short length or insufficient information in some texts. |
| Approach: | They propose a method that leverages external text corpora to evaluate the difference in logit distribution of input text under retrieved human-written and LLM-rewritten contexts. |
| Outcome: | The proposed method achieves state-of-the-art performance in AUROC on five public datasets with three widely-used source LLMs. |
Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding. |
| Approach: | They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets. |
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)
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| 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. |
Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM (2025.findings-emnlp)
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| Challenge: | Existing methods for unimodal large language models are inadequate for MLLMs due to multimodal data complexity and multi-phase training. |
| Approach: | MM-DETECT analyzes data contamination using a framework that defines two contamination categories - unimodal and cross-modal . |
| Outcome: | The proposed framework quantifies contamination severity across multiple-choice and caption-based Visual Question Answering tasks. |
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2025.acl-long)
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| Challenge: | Existing methods focus excessively on detection accuracy, neglecting the societal risks posed by high false positive rates (FPRs). |
| Approach: | They propose a Conformal Prediction framework that constrains the upper bound of false positive rates and introduces a real-time detection framework. |
| Outcome: | The proposed framework reduces false positive rates and improves detection performance. |
How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study (2025.findings-emnlp)
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| Challenge: | Recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%, but these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies. |
| Approach: | They examine how sampling-based decoding impacts detectability with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |
| Outcome: | The proposed framework systematically examines how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |
A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection (2025.emnlp-main)
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| Challenge: | Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. |
| Approach: | They propose a framework that implements an adversarial training paradigm by an agent symbolic learning optimization process rather than numerical updates. |
| Outcome: | The proposed framework generates sophisticated fake news that degrades state-of-the-art detection performance by 53.4% in Chinese and 34.2% in English on average. |
Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024.emnlp-main)
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| Challenge: | scalable strategies to combat online misinformation are short-term and insufficient, authors say . current reactive approaches, like content flagging and banning, do little to change perception of misinformants . human evaluations show that our framework generates expert-like responses . |
| Approach: | They propose a framework that generates persuasive responses from hate-speech counter-responses . human evaluations show that the framework generates expert-like responses . |
| Outcome: | The proposed framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives. |
Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)
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| Challenge: | Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities. |
| Approach: | They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities. |
| Outcome: | The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection. |
AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)
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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng
| Challenge: | Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content. |
| Approach: | They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories . |
| Outcome: | The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources. |
Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)
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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang
| Challenge: | Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials. |
| Approach: | They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located. |
| Outcome: | The proposed method outperforms previous detection methods by large margins on three text classification tasks. |
EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods to detect LLM-generated texts rely on static benchmarks that neglect the evolving nature of LLMs. |
| Approach: | They propose a benchmark to evaluate the generalization of LLM-generated text detection methods. |
| Outcome: | The proposed benchmark measures generalization of 14 detection methods across LLMs. |
Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models (2025.emnlp-main)
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| Challenge: | Despite advances in large language models, their application to misinformation detection remains hindered by issues of logical inconsistency and superficial verification. |
| Approach: | They propose a multi-agent debate framework that reformulates misinformation detection as a structured adversarial debate based on fact-checking workflows . |
| Outcome: | The proposed framework enables iterative refinement of evidence while improving decision transparency. |
SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)
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| Challenge: | Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution . |
| Approach: | They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks. |
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models (2025.findings-acl)
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Shuliang Liu, Hongyi Liu, Aiwei Liu, Duan Bingchen, Zheng Qi, Yibo Yan, He Geng, Peijie Jiang, Jia Liu, Xuming Hu
| Challenge: | Existing methods for detection of misinformation generated by large language models fail to mitigate societal risks . authors propose a paradigm shift from passive detection to anticipatory mitigation strategies . existing defenses remain reactionary in an era demanding proactive defense, authors say . |
| Approach: | They propose a three-pillar approach to prevent misinformation by fortifying integrity of training data and inference reliability by embedding self-corrective mechanisms during reasoning. |
| Outcome: | The proposed framework improves existing methods in misinformation prevention by 63% . it demonstrates that existing methods exhibit false negative rates against misinformation . |
Data Contamination Can Cross Language Barriers (2024.emnlp-main)
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| Challenge: | Existing methods to detect contamination of public benchmarks are too superficial to reflect deeper forms of contamination. |
| Approach: | They propose generalization-based approaches to unmask a cross-lingual form of contamination that inflates LLMs’ performance while evading current detection methods. |
| Outcome: | The proposed model outperforms existing detection methods while avoiding contamination of public benchmarks in the pre-training data. |
Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) generate convincing career trajectories in fake resumes . a novel heterogeneous, hierarchical multi-layer graph framework is proposed to model career entities and their relations in a unified global graph built from genuine resumes. |
| Approach: | They propose a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes. |
| Outcome: | The proposed framework outperforms state-of-the-art models by 5.8-85.0% relative to baselines. |
LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection (2026.acl-long)
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| Challenge: | Recent detection methods struggle to capture fine-grained semantic differences, especially for short texts. |
| Approach: | They propose a framework for machine-revised text detection that integrates two modules to enhance discriminative semantic features. |
| Outcome: | The proposed method outperforms existing detectors in identifying machine-revised text across diverse practical scenarios, tasks, and LLMs. |
Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection (2026.acl-long)
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| Challenge: | Existing methods for AI-generated text detection assume uniform token contributions, making them less robust under short sequences or localized token modifications. |
| Approach: | They propose a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective. |
| Outcome: | The proposed method achieves state-of-the-art detection performance and robustness to adversarial attacks and varying input lengths. |
Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods for fake news detection rely on monolithic verification methods . Existing approaches often yield ambiguous verdicts due to superficial processing . |
| Approach: | They propose a protocol-adaptive role-specific multi-agent framework that decomposes verification into factual, logical, and contextual dimensions. |
| Outcome: | The proposed framework outperforms baseline methods in both predictive accuracy and explanatory quality. |
Profiler: Black-box AI-generated Text Origin Detection via Context-aware Inference Pattern Analysis (2025.emnlp-main)
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Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Guangyu Shen, Kaiyuan Zhang, Shengwei An, Guanhong Tao, Xiangyu Zhang
| Challenge: | Existing methods to identify the origin of AI-generated texts fail to identify origin due to the high similarity of different LLMs. |
| Approach: | They propose a black-box AI-generated text origin detection method which accurately predicts the origin of an input text by extracting distinct context inference patterns. |
| Outcome: | The proposed method outperforms 10 state-of-the-art baselines and achieves a 25% increase in AUC score on average across natural language and code datasets. |
CSI: An Investigative Multi-Agent Framework for Explainable Short Video Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods for short video fake news detection rely on black-box MSLMs with poor explainability and superficial understanding or on specific prompt strategies for Multimodal Large Language Models (MLLMs) |
| Approach: | They propose a multi-agent framework called CSI for short video fake news detection. |
| Outcome: | The proposed framework provides rigorous explanations while achieving state-of-the-art performance on two real-world datasets. |
UMPIRE: Unveiling LLM-generated Posts via Redundant Expressions (2026.acl-long)
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| Challenge: | Existing methods to verify the provenance of multimodal content fall into two categories: traditional methods rely on low-level artifacts or unimodal statistics. |
| Approach: | They propose a semantic decomposition mechanism that disentangles textual embeddings into redundant and complementary components and a latent redundancy regularization loss to encourage LLM-generated content to exhibit high semantic redundancies. |
| Outcome: | The proposed method outperforms state-of-the-art detection methods across multiple datasets and achieves 5.38% improvement in accuracy. |
CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages (2026.findings-acl)
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| Challenge: | Existing methods for machine-generated text detection are mostly focused on English . existing methods are almost unusable for non-English languages, leaving the transferability towards these languages unexplored. |
| Approach: | They propose to use a train-language combination to compare MGT detection methods . they focus on multi-domain, multi-generator, and multilingual evaluation . |
| Outcome: | The proposed methods are the most performant in the Central European languages and resistant against obfuscation. |
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)
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| 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. |
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims (2025.acl-long)
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| Challenge: | Identifying checkworthy claims is the first step, but detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. |
| Approach: | They propose a dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. |
| Outcome: | The proposed dataset compares lightweight text-based encoders to multimodal models but only focus on claim-like content. |
Why Do Multilingual Reasoning Gaps Emerge in Reasoning Language Models? (2026.findings-acl)
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| Challenge: | Recent reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap. |
| Approach: | They propose a strategy that incorporates an English translation into the initial reasoning trace when an understanding failure is detected. |
| Outcome: | The proposed strategy incorporates an English translation into the initial reasoning trace when an understanding failure is detected. |
Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity (2026.acl-long)
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| Challenge: | Existing methods for detecting LLM-generated text rely on statistical features that are insufficient for reliable detection. |
| Approach: | They propose a temperature-sensitive detector that modulates decoding temperature and monitors how probability distributions respond to temperature. |
| Outcome: | The proposed method is based on a temperature sensitivity feature and a simple zero-shot detector built upon normalized temperature sensitivity. |
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs (2025.emnlp-main)
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Denis Janiak, Jakub Binkowski, Albert Sawczyn, Bogdan Gabrys, Ravid Shwartz-Ziv, Tomasz Jan Kajdanowicz
| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their tendency to hallucinate poses serious challenges for reliable deployment. |
| Approach: | They propose to use ROUGE to assess lexical overlap to determine accuracy of hallucination detection methods. |
| Outcome: | The proposed evaluation frameworks can rival complex methods, exposing a fundamental flaw in current evaluation practices. |
When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing (2026.acl-long)
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| Challenge: | sexist content on social media is increasingly pervasive, often appearing in subtle, context-dependent forms that evade traditional classification methods. |
| Approach: | They propose a framework that unifies targeted training procedures to regularize supervision to scarce and noisy data with selective reasoning-based inference to handle ambiguous or borderline cases. |
| Outcome: | The proposed framework outperforms existing approaches across several public benchmarks . it bridges the gap between efficiency and reasoning with a dynamic routing mechanism . |
Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps (2026.findings-acl)
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| Challenge: | Existing methods for hallucination detection for text-based LLMs do not capture audio-specific signals. |
| Approach: | They propose to capture pathological attention patterns associated with hallucination using four attention-derived metrics to train lightweight logistic regression classifiers. |
| Outcome: | The proposed approach outperforms baselines on in-domain data and generalises to out-of-domain ASR settings. |