Papers with out-of-distribution scenarios
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. |
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty? (2025.acl-short)
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| Challenge: | Large language models (LLMs) are increasingly used in high-stakes domains, but their confidence is inconsistent in out-of-distribution scenarios. |
| Approach: | They define "marker confidence" as the observed accuracy when a model employs an epistemic marker. |
| Outcome: | The proposed model generalizes well within the same distribution, but its confidence is inconsistent in out-of-distribution scenarios. |
Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
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Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Large language models (LLMs) are not specifically trained to deal with ambiguous utterances . ambiguity can lead to varying interpretations of the same input based on different assumptions or background knowledge . |
| Approach: | They propose a pipeline that aligns large language models to manage ambiguous queries . they propose to use their own assessment of perceived ambiguity to detect and manage queries a . |
| Outcome: | Experimental results show that APA empowers LLMs to detect and manage ambiguous queries while retaining the ability to answer clear questions. |
CrisiText: A dataset of warning messages for LLM training in emergency communication (2026.findings-eacl)
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| Challenge: | Identifying threats and mitigating their potential damage during crisis situations is paramount for safeguarding endangered individuals. |
| Approach: | They present a large-scale dataset for the generation of warning messages across 13 different types of crisis scenarios. |
| Outcome: | The proposed dataset contains more than 400,000 warning messages (spanning almost 18,000 crisis situations) aimed at assisting civilians during and after such events. |
GuardEmb: Dynamic Watermark for Safeguarding Large Language Model Embedding Service Against Model Stealing Attack (2024.findings-emnlp)
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| Challenge: | Recent studies reveal the risk of the model stealing attack, posing a financial threat to EaaS providers. |
| Approach: | They propose a dynamic embedding watermarking method that detects watermarks in embedded text . this method is a cross-platform approach that trains a verifier to detect watermark . |
| Outcome: | The proposed method enables an attacker to replicate the proposed method for profit without compromising embedding functionality. |