7 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…
From Training to Generalization: Improving Moral Reasoning Through Pragmatic Inference
Guangliang Liu, Xi Chen, Bocheng Chen +3
Although moral reasoning has emerged as a promising research direction for large language models (LLMs), a persistent generalization challenge remains: LLMs often achieve strong pe…
Can Large Language Models Handle Discourse Particles? A Case Study of Colloquial Malay
Mariah Al Giptiah Binte Yusoff, Jakin Tan, Bocheng Chen +2
Discourse particles, such as \textit{well} and \textit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to convey emotions, intentions…
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…
Diagnosing the Performance Trade-off in Moral Alignment: A Case Study on Gender Stereotypes
Guangliang Liu, Bocheng Chen, Han Zi +2
Moral alignment has emerged as a widely adopted approach for regulating the behavior of pretrained language models (PLMs), typically through fine-tuning on curated datasets. Gender…