1 citations · 2 across the 14 of their papers we have counts for
6 papers · 2 filters
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…