| Challenge: | Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving, but their potential in authorship analysis remains under-explored. |
| Approach: | They propose to integrate explicit linguistic features into LLMs to provide explanations into their reasoning processes. |
| Outcome: | The proposed models demonstrate their ability to perform zero-shot, end-to-end authorship verification effectively and provide explainability through explicit linguistic features. |
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators. |
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Open-World Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models do not evaluate their performance in academic research . authors aim to identify authors from anonymous text without additional information . |
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A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
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The Two Paradigms of LLM Detection: Authorship Attribution vs Authorship Verification (2025.findings-acl)
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| Challenge: | Existing methods for detecting texts generated by large language models are disputed . authors argue that there are limitations in the current technology . |
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Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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Factuality of Large Language Models: A Survey (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
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Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
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