Papers with detection process
Detecting Machine-Generated Text: Techniques and Challenges (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on machine-generated text and deepfakes. |
| Approach: | This tutorial aims to provide a comprehensive overview of text detection techniques . it will focus on machine-generated text and deepfakes . |
| Outcome: | This tutorial focuses on machine-generated text and deepfakes. |
A Robust Semantics-based Watermark for Large Language Model against Paraphrasing (2024.findings-naacl)
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| Challenge: | Existing methods to detect LLM-generated content use simple hashes of precedent tokens to partition vocabulary. |
| Approach: | They propose a semantics-based watermark framework to enhance the robustness against paraphrase. |
| Outcome: | The proposed framework is robust under different paraphrases and the semantic meaning of the sentences will be likely preserved under paraphrase. |
Probing LLMs for hate speech detection: strengths and vulnerabilities (2023.findings-emnlp)
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| Challenge: | Recent efforts to detect hateful or toxic language using large language models have not used explanation, additional context and victim community information in the detection process. |
| Approach: | They use different prompt variations, input information and victim community information to evaluate large language models in zero shot setting without adding any in-context examples. |
| Outcome: | The proposed models perform significantly better when included in the pipeline than baseline models. |
Learning to Focus on the Foreground for Temporal Sentence Grounding (2022.coling-1)
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| Challenge: | Existing methods for temporal sentence grounding do not capture subtle details of small objects. |
| Approach: | They propose a detection-free framework for temporal sentence grounding that learns to locate foreground regions related to the query in consecutive frames. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three challenging datasets. |
An Entropy-based Text Watermarking Detection Method (2024.acl-long)
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| Challenge: | Existing text watermarking algorithms for large language models (LLMs) are effective in identifying machine-generated texts, but they are not effective in low-entropy scenarios. |
| Approach: | They propose an Entropy-based text watermarking detection method that takes into account the influence of token entropy to better reflect the degree of watermark detection. |
| Outcome: | The proposed method is training-free and fully automated. |
Perception, Understanding and Reasoning: A Multimodal Benchmark for Video Fake News Detection (2026.acl-long)
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| 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. |
RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection (2026.acl-long)
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| Challenge: | a new framework for hate speech detection addresses implicit hate speech by tailoring the detection process to dataset-specific attributes. |
| Approach: | They propose a framework to account for the dataset-specific characteristics of hate speech datasets. |
| Outcome: | The proposed framework improves detection accuracy and provides interpretable insights into the distinctive features of each dataset. |
A Linguistics-Aware LLM Watermarking via Syntactic Predictability (2026.acl-long)
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| Challenge: | a central challenge remains balancing text quality against detection robustness. |
| Approach: | They propose a framework that aligns watermark strength with linguistic degrees of freedom . they use part-of-speech models to weaken the signal in grammatically constrained contexts . |
| Outcome: | The proposed framework outperforms existing methods in linguistic indeterminacy tests on languages . it weakens the watermark strength in grammatically constrained contexts and strengthens it in contexts with greater linguistic flexibility. |