1 citations · 3 across the 14 of their papers we have counts for
15 papers
When Words Speak Louder than Images: Towards Understanding Language Bias in Vision-Language Models
Yizhou Fang, Siyue Chen, Zimo Qi +3
Despite substantial progress across downstream applications, vision-language models (VLMs) remain susceptible to language bias, often prioritizing linguistic cues over visual evide…
When Humans Leave Context Unsaid: Implicit Context as a Risk for Large Language Models
Bocheng Chen, Han Zi, Yuda Chen +6
Human language is grounded in pragmatics, necessitating implicit (extralinguistic) context to interpret language, e.g., world knowledge and social norms. However, such context is o…
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…
On the Convergence of Moral Self-Correction in Large Language Models
Guangliang Liu, Haitao Mao, Bochuan Cao +4
Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only a general and abstr…
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…
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…
No Free Lunch for Defending Against Prefilling Attack by In-Context Learning
Zhiyu Xue, Guangliang Liu, Bocheng Chen +2
The security of Large Language Models (LLMs) has become an important research topic since the emergence of ChatGPT. Though there have been various effective methods to defend again…
Smaller Large Language Models Can Do Moral Self-Correction
Guangliang Liu, Zhiyu Xue, Xitong Zhang +2
Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feed…
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…
Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis
Guangliang Liu, Haitao Mao, Jiliang Tang +1
Large Language Models (LLMs) are capable of producing content that perpetuates stereotypes, discrimination, and toxicity. The recently proposed moral self-correction is a computati…
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness
Guangliang Liu, Milad Afshari, Xitong Zhang +5
While task-agnostic debiasing provides notable generalizability and reduced reliance on downstream data, its impact on language modeling ability and the risk of relearning social b…
On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept
Guangliang Liu, Haitao Mao, Bochuan Cao +5
Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only the task's goal wit…
A Survey to Recent Progress Towards Understanding In-Context Learning
Haitao Mao, Guangliang Liu, Yao Ma +3
In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the…
PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent
Guangliang Liu, Zhiyu Xue, Xitong Zhang +2
Fine-tuning pretrained language models (PLMs) for downstream tasks is a large-scale optimization problem, in which the choice of the training algorithm critically determines how we…
Improving Generalization of Complex Models under Unbounded Loss Using PAC-Bayes Bounds
Xitong Zhang, Avrajit Ghosh, Guangliang Liu +1
Previous research on PAC-Bayes learning theory has focused extensively on establishing tight upper bounds for test errors. A recently proposed training procedure called PAC-Bayes t…