Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing (2026.findings-acl)
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| Challenge: | Large language models have been used in reasoning-heavy planning tasks but their tendency to hallucinate unsafe outputs poses risks. |
| Approach: | They propose a method for searching the space of prompt perturbations using adaptive stress testing with Monte-Carlo tree search. |
| Outcome: | The proposed method can detect scenarios, sensor configurations, and prompt phrasing that cause models to act with high uncertainty or crash. |
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| Challenge: | Large language models (LLMs) often generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. |
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| Challenge: | Language models display sensitivity to input perturbations, causing concerns about trust among users. |
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Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
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| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
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Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
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Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
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| Challenge: | Existing studies on the robustness of LLMs with few-shot prompting techniques are limited. |
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Uncertainty Quantification for Large Language Models (2025.acl-tutorials)
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| Challenge: | Large language models (LLMs) produce hallucinations, which undermine user trust and reliability. |
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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. |
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Exploring the Sensitivity of LLMs’ Decision-Making Capabilities: Insights from Prompt Variations and Hyperparameters (2023.findings-emnlp)
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| Challenge: | Prior studies have compared the decision-making abilities of large language models with those of humans from a psychological perspective. |
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The Shape of Vulnerability: How Adversarial Perturbations Reshape the Topology of Language Model Latent Spaces (2026.acl-srw)
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Angelina Tsai, Shreya Subramanian, Catherine Liu, Kimberly Lopez, Leif Zinn-Brooks, Alexia E. Schulz, Adaku Uchendu
| Challenge: | Large Language Models (LLMs) have unprecedented capabilities, but they pose security concerns . current adversarial attacks exploit vulnerabilities in the embedding space of language models, allowing attackers to bypass safety guardrails and cause significant harmful consequences. |
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Calibrating LLM Confidence by Probing Perturbed Representation Stability (2025.emnlp-main)
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Reza Khanmohammadi, Erfan Miahi, Mehrsa Mardikoraem, Simerjot Kaur, Ivan Brugere, Charese Smiley, Kundan S Thind, Mohammad M. Ghassemi
| Challenge: | Despite their impressive performance, large language models (LLMs) consistently struggle with confidence calibration. |
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