DETECTION ACCURACY

Unsupervised Out-of-Domain Detection via Pre-trained Transformers (2021.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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Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng, Caiming Xiong

WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents (2025.findings-naacl)

Challenge:	Prior work on out-of-domain detection requires in-domain task labels and is limited to supervised classification scenarios.
Approach:	They propose a method to construct out-of-domain detectors efficiently using pre-trained transformers.
Outcome:	The proposed method greatly improves out-of-domain detection ability in a more general scenario.

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Leyi Pan, Aiwei Liu, Yijian Lu, Zitian Gao, Yichen Di, Lijie Wen, Irwin King, Philip S. Yu

Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only brief segments within longer documents.
Approach:	They propose a method to detect watermarked segments in large documents using an anomaly extraction method and a local traversal.
Outcome:	The proposed method achieves a superior balance between detection accuracy and computational efficiency.

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Jinwen Chen, Hainan Zhang, Fei Sun, Qinnan Zhang, Sijia Wen, Ziwei Wang, Zhiming Zheng

Robustness in NLP

Challenge:	Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers.
Approach:	They propose to efficiently remove poisoned examples before or during fine-tuning .
Outcome:	The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset.

RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.emnlp-main)

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Pingyi Hu, Xiaofan Bai, Xiaojing Ma, Chaoxiang He, Dongmei Zhang, Bin Benjamin Zhu

What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (2023.eacl-main)

Challenge:	Existing methods for tamper detection rely on model stability, not inherently stochastic models.
Approach:	They propose a hypothesis-testing method for black-box tamper detection for LLMs . they propose regularized entropy-sensitive fingerprinting to enable efficient fingerprinting .
Outcome:	The proposed method achieves 98.80% detection accuracy under challenging conditions . it is based on a first-order surrogate for KL divergence to identify prompts most responsive to parameter perturbations.

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Tom Bourgeade, Patricia Chiril, Farah Benamara, Véronique Moriceau

FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter (2025.findings-emnlp)

Challenge:	Hate speech detection datasets often use different annotation guidelines, resulting in inconsistencies . authors propose a topic-oriented approach to study generalization across popular hate speech datasets .
Approach:	They propose a topic-oriented approach to study generalization across popular hate speech datasets . they compare Transformer-based models in capturing topic-generic and topic-specific knowledge .
Outcome:	The proposed approach improves the reliability of hate speech detection on social media platforms.

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JunXi Wang, Yaxiong Wang, Lechao Cheng, Zhun Zhong

SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion (2025.emnlp-main)

Challenge:	Existing methods for detecting fake news videos fall short due to lack of knowledge to verify the news is real or not.
Approach:	They propose a VLM-based framework for detecting fake news on short video platforms . they design four experts tailored to handle each scenario and integrate them into VLM .
Outcome:	The proposed framework outperforms current state-of-the-art models on two benchmark datasets.

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Lei Jiang, Desheng Wu, Xiaolong Zheng

Text Generation

Challenge:	Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse.
Approach:	They propose a framework for sentence-level AI-generated text detection via style and context fusion.
Outcome:	The proposed framework outperforms baseline models in detection accuracy while exhibiting transferability and robustness.

Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)

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Yuang Li, Min Zhang, Mengxin Ren, Xiaosong Qiao, Miaomiao Ma, Daimeng Wei, Hao Yang

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)

Challenge:	Existing audio deepfake detection datasets are outdated and lack generalization capabilities.
Approach:	They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models.
Outcome:	The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models.

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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang

What’s Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews (2026.acl-long)

Challenge:	prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems.
Approach:	They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference.
Outcome:	The proposed model maintains safety while reducing over-refusal.

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Fanxiao Li, Jiaying Wu, Tingchao Fu, Dayang Li, Herun Wan, Wei Zhou, Min-Yen Kan

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)

Challenge:	Existing efforts to detect factually incorrect content are omitted by creators who subtly reshape impressions by omitting crucial background context.
Approach:	They propose a multi-stage pipeline that simulates preview-based and context-based understanding and a OMGuard pipeline that combines interpretation-aware fine-tuning and rationale-guided misleading content correction.
Outcome:	The proposed framework lifts an 8B model’s detection accuracy to the level of a 235B LVLM while delivering stronger end-to-end correction.

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Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, Hongfei Lin

LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing (2025.acl-long)

Challenge:	Existing studies focus on evaluating large language models' ability to handle disagreement cases.
Approach:	They evaluate the performance of large language models in detecting offensive language at varying levels of agreement.
Outcome:	The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training.

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Dario Di Palma, Alessandro De Bellis, Giovanni Servedio, Vito Walter Anelli, Fedelucio Narducci, Tommaso Di Noia

Semantic Text Processing

Challenge:	Large Language Models (LLMs) have become central to NLP, demonstrating their ability to adapt to various tasks through prompting techniques.
Approach:	They probe the hidden layers of Large Language Models to identify where sentiment features are most represented and to assess how this affects sentiment analysis.
Outcome:	The proposed approach enables sentiment tasks to be performed with memory requirements reduced by an average of 57%.

Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (2020.findings-emnlp)

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Łukasz Borchmann, Dawid Wisniewski, Andrzej Gretkowski, Izabela Kosmala, Dawid Jurkiewicz, Łukasz Szałkiewicz, Gabriela Pałka, Karol Kaczmarek, Agnieszka Kaliska, Filip Graliński

Semantic Text Processing

Challenge:	Existing methods for detecting text fragments are not suitable for contract discovery, since it requires manual definition of a few examples, followed by conventional information.
Approach:	They propose a task where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.
Outcome:	The proposed task differs substantially from conventional NLI and shared tasks on legal information extraction.

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025.findings-acl)

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Fanxiao Li, Jiaying Wu, Canyuan He, Wei Zhou

SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information (2025.findings-acl)

Challenge:	Multimodal large language models have demonstrated impressive capabilities in visual reasoning and text generation.
Approach:	They propose a multimodal large language model that captures deeper relationships between images and text . they propose CMIE, which uses a Coexistence Relationship Generation strategy and an AS mechanism to detect misinformation.
Outcome:	The proposed framework outperforms existing methods in detecting out-of-context misinformation.

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Xiangyu Zhang, Hexin Liu, Qiquan Zhang, Beena Ahmed, Julien Epps

Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

Challenge:	Large Language Models (LLMs) have been extensively utilized for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input.
Approach:	They propose a system that leverages speech timing features for depression detection and reliable confidence estimation.
Outcome:	The proposed system outperforms text-based RAG systems in depression detection and confidence estimation.

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Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li

Semantic Text Processing

Challenge:	Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach:	They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome:	The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.

A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)

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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

Challenge:	Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance .
Approach:	They propose to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and advocating for more adaptable and robust models to enhance detection accuracy.
Outcome:	The proposed model will be able to detect human-written content in real time.

RedHerring Attack: Testing the Reliability of Attack Detection (2025.emnlp-main)

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Jonathan Rusert

Robustness in NLP

Challenge:	In response to adversarial text attacks, attack detection models have been proposed and shown to successfully identify text modified by adversaries.
Approach:	They propose to modify a text to cause the detection model to predict an attack while keeping the classifier correct.
Outcome:	The proposed model drops detection accuracy between 20 - 71 points while maintaining (or improving) classifier accuracy.

Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2025.acl-long)

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Xiaowei Zhu, Yubing Ren, Yanan Cao, Xixun Lin, Fang Fang, Yangxi Li

LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)

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.

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Jian Lang, Rongpei Hong, Meihui Zhong, Kaiju Li, Ting Zhong, Qiang Gao, Fan Zhou

Low-Entropy Watermark Detection via Bayes’ Rule Derived Detector (2025.findings-acl)

Challenge:	Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions.
Approach:	They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos .
Outcome:	The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks.

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Beining Huang, Du Su, Fei Sun, Qi Cao, Huawei Shen, Xueqi Cheng

Numerical Reasoning

Challenge:	Existing methods for text watermarking ignore strong evidences embedded in low-entropy tokens, causing statistical measures to falsely indicate the absence of a watermark.
Approach:	They propose a Bayes' Rule derived watermark Detector which exploits watermark information from every token by leveraging the posterior probability of watermark’s presence.
Outcome:	The proposed method achieves 50% and 70% relative improvements over baselines in code generation and math problem-solving tasks.

Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)

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Jiashu Pu, Ziyi Huang, Yadong Xi, Guandan Chen, Weijie Chen, Rongsheng Zhang

Text Generation

Challenge:	Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions.
Approach:	They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features.
Outcome:	The proposed method replaces text with linguistic or statistical features and improves the accuracy of the model.

What You Read Isn’t What You Hear: Linguistic Sensitivity in Deepfake Speech Detection (2025.emnlp-main)

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Binh Nguyen, Shuju Shi, Ryan Ofman, Thai Le

MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Recent advances in text-to-speech technology have enabled highly realistic voice generation, fueling deepfake attacks.
Approach:	They propose a framework for transcript-to-audio perturbation anti-spoofing that incorporates linguistic variation into detectors to investigate spoof detection.
Outcome:	The proposed framework can bypass commercial detectors by incorporating linguistic variation into the design of anti-spoofing systems.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Yayue Deng, Jing Ma

Breaking the "Provable Security": Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing (2026.findings-acl)

Challenge:	Existing evaluation approaches focus on mLLMs’ detection accuracy for binary classification tasks, which often fail to reflect the in-depth interpretive nuance of harmfulness across diverse contexts.
Approach:	They propose an agent-based arena-style evaluation framework that provides context-aware and unbiased assessment for mLLMs’ understanding of multimodal harmfulness.
Outcome:	The proposed framework reduces evaluation biases of judge agents and provides unbiased comparisons of mLLMs’ abilities to interpret multimodal harmfulness.

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Wenzhao Cao, Yaofei Wang, Donghui Hu

Defending against Indirect Prompt Injection by Instruction Detection (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim “provably secure” by theoretically aligning the stego distribution with the language model’s natural distribution.
Approach:	They propose a framework that transforms the detection task from semantic classification to a statistical audit of the sampling mechanism.
Outcome:	The proposed framework breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing.

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Tongyu Wen, Chenglong Wang, Xiyuan Yang, Haoyu Tang, Yueqi Xie, Lingjuan Lyu, Zhicheng Dou, Fangzhao Wu

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? (2026.acl-long)

Challenge:	Indirect Prompt Injection attacks can be exploited by LLMs that are embedded with external data.
Approach:	They propose a detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks.
Outcome:	The proposed approach reduces the success rate of attacks to 0.03% on the BIPIA benchmark.

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Fengqing Jiang, Yichen Feng, Yuetai Li, Luyao Niu, Basel Alomair, Radha Poovendran

Challenge:	Existing evidence suggests that LLMs are not able to detect scientifically unsound work from malicious or poorly designed research agents.
Approach:	They develop a framework that evaluates whether fabrication-oriented paper generation agents can deceive multi-model LLM review systems.
Outcome:	The proposed framework shows that fabricated papers achieve acceptance rates up to 18% . the framework shows only marginal improvements, with detection accuracy barely exceeding random chance.

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning (2026.acl-long)

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Zhihui Chen, Kai He, Qingyuan Lei, Bin Pu, Jian Zhang, Yuling Xu, Mengling Feng

Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling (2025.acl-long)

Challenge:	Existing defenses against forgery are inadequate for healthcare.
Approach:	They propose a large-scale benchmark for pre-hoc, evidence-grounded medical forgery detection using a doctor inspection guideline and gold edit locations.
Outcome:	Experiments show that the proposed solution can detect and explain medical scans with high fidelity and accuracy.

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Zhao Tong, Yimeng Gu, Huidong Liu, Qiang Liu, Shu Wu, Haichao Shi, Xiao-Yu Zhang

Reasoning

Challenge:	Existing models have a performance gap of 20% between classifying fake news and real news, making them less suitable for practical deployment.
Approach:	They propose to adopt an LLM to generate fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news.
Outcome:	The proposed model achieves state-of-the-art performance on two benchmark datasets and improves detection accuracy by 24.02% and 11.06% respectively.

TABARD: A Novel Benchmark for Tabular Anomaly Analysis, Reasoning and Detection (2025.findings-emnlp)

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Manan Roy Choudhury, Anirudh Iyengar Kaniyar Narayana Iyengar, Shikhhar Siingh, Sugeeth Puranam, Vivek Gupta

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)

Challenge:	a new benchmark is constructed to evaluate the accuracy of large language models for tabular data . the benchmark uses direct, indirect, and Chain-of-Thought prompting .
Approach:	They propose a framework that uses prompting, self-verification and constraint-based rule execution to improve accuracy.
Outcome:	The proposed framework significantly improves accuracy and recall in tabular data.

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Ke Yang, Dongyang Liang, Jing Yu, Shuguang Yuan, Chi Chen

Summary Factual Inconsistency Detection Based on LLMs Enhanced by Universal Information Extraction (2025.findings-acl)

Challenge:	Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary.
Approach:	They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution.
Outcome:	The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity.

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Anguo Li, Lei Yu

Information Extraction & Text Mining

Challenge:	Recent studies have shown that Large language models can detect factual inconsistencies in summaries but they lack the efficiency and explainability needed to be effective.
Approach:	They propose to decouple LLMs’ information extraction and reasoning capabilities to address key challenges and propose a framework for UIEFID to guide fine-tuned LLM methods in extracting unified structured information from documents and summaries.
Outcome:	The proposed framework improves the detection accuracy and reduces redundant reasoning on the AGGREFACT benchmark.

TempParaphraser: “Heating Up” Text to Evade AI-Text Detection through Paraphrasing (2025.emnlp-main)

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Junjie Huang, Ruiquan Zhang, Jinsong Su, Yidong Chen

Text Generation

Challenge:	Existing detectors that perform well on benchmark datasets have weaknesses that can be exploited to manipulate AI-text.
Approach:	They propose a framework that simulates high-temperature sampling effects through multiple normal-temperaturing generations, effectively evading detection.
Outcome:	The proposed framework reduces detector accuracy by an average of 82.5% while preserving high text quality.

Perception, Understanding and Reasoning: A Multimodal Benchmark for Video Fake News Detection (2026.acl-long)

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Cui Yakun, Peng Qi, Fushuo Huo, Hang Du, Weijie Shi, Juntao Dai, Zhenghao Zhu, Sirui Han

RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection (2026.acl-long)

Challenge:	Existing video fake news detection benchmarks focus on the detection accuracy, while failing to provide fine-grained assessments for the entire detection process.
Approach:	They propose a process-oriented video fake news detection benchmark that evaluates MLLMs' perception, understanding, and reasoning capabilities in VFND.
Outcome:	The proposed model achieves sota performance on video fake news detection tasks.

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Yejin Lee, Hyeseon An, Yo-Sub Han