6 papers
Pragmatic Attack Surface: Vulnerabilities of Implicit Context in Large Language Models
Bocheng Chen, Han Zi, Roucheng Ou +5
In the era of large language models (LLMs), attackers often manipulate natural language to elicit unsafe or harmful outputs, creating a new natural language attack surface unique t…
Learning to Diagnose and Correct Moral Errors: Beyond Shallow Heuristics in Moral Alignment
Bocheng Chen, Xi Chen, Han Zi +5
Existing approaches to moral value alignment are primarily set out to align LLMs' generation with the distributions of morally appropriate language, which has seen good progress. H…
Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment
Zhiyu Xue, Zimo Qi, Guangliang Liu +2
Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal answers. Although safety alignment…
Self-correction is Not An Innate Capability in Language Models
Guangliang Liu, Zimo Qi, Xitong Zhang +2
Although there has been growing interest in the self-correction capability of Large Language Models (LLMs), there are varying conclusions about its effectiveness. Prior research ha…
Discourse Heuristics For Paradoxically Moral Self-Correction
Guangliang Liu, Zimo Qi, Xitong Zhang +1
Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models (LLMs) with human moral values. However, moral self-correction techniques…
Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization
Guangliang Liu, Zimo Qi, Xitong Zhang +2
Ensuring that Large Language Models (LLMs) return just responses which adhere to societal values is crucial for their broader application. Prior research has shown that LLMs often…