1 citations · 4 across the 11 of their papers we have counts for
11 papers
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…
How Far Can Sharpness and Complexity Jointly Explain Generalization?
Ziyu Cheng, Xitong Zhang, Longxiu Huang +1
Large-scale empirical studies have revealed that many widely used generalization measures correlate only weakly with generalization, suggesting that relying on a single factor is i…
Toward Global Large Language Models in Medicine
Rui Yang, Huitao Li, Weihao Xuan +48
Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…
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…
AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data
Xinjie Zhao, Moritz Blum, Rui Yang +10
Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsis…
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…
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…
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition
Xitong Zhang, Ismail R. Alkhouri, Rongrong Wang
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNN…
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…