Papers with remediation

3 papers
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes (2024.naacl-long)

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Challenge: Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes.
Approach: They propose a method that uses cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation.
Outcome: The proposed model can bridge the novice-expert knowledge gap by using cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation.
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL (2026.findings-acl)

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Challenge: Large Language Models (LLMs) have been successful in Text-to-SQL tasks, but their deployment in real-world environments is hindered by latent reliability issues.
Approach: They propose a framework to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation.
Outcome: The proposed framework uncovers a substantial number of failure cases on state-of-the-art open-source LLMs.
Activation Decomposition and Steering for LLM Backdoor Remediation (2026.acl-long)

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Challenge: Existing approaches to defending against LLM backdoors rely on auxiliary models or safety-related datasets.
Approach: They propose a method which contrasts benign and poisoned settings to decompose feature vectors for steering without auxiliary models or datasets.
Outcome: The proposed method achieves better defense qualities than existing steering strategies.

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