25 citations · 54 across the 18 of their papers we have counts for
18 papers
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
Zhuo Li, Pengyu Cheng, Zhechao Yu +7
Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonl…
Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
Wenya Xie, Qingying Xiao, Yu Zheng +8
The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain…
APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport
Zhuo Li, Yuege Feng, Dandan Guo +3
The reward model (RM) plays a crucial role in aligning Large Language Models (LLMs) with human preferences through Reinforcement Learning, where the Bradley-Terry (BT) objective ha…
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm
Zhuo Li, Yuhao Du, Xiaoqi Jiao +5
Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the performance of Large Language Model…
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
Yuhao Du, Zhuo Li, Pengyu Cheng +4
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning Large Language Models (LLMs) with human values. However, RLHF has been continuously challenged by its high…
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
Zhuo Li, Yuhao Du, Jinpeng Hu +2
Large language models (LLMs) have shown success in generating high-quality responses. In order to achieve better alignment with LLMs with human preference, various works are propos…
Aligning Language Models Using Follow-up Likelihood as Reward Signal
Chen Zhang, Dading Chong, Feng Jiang +4
In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal respons…
Atoxia: Red-teaming Large Language Models with Target Toxic Answers
Yuhao Du, Zhuo Li, Pengyu Cheng +2
Despite the substantial advancements in artificial intelligence, large language models (LLMs) remain being challenged by generation safety. With adversarial jailbreaking prompts, o…
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
Junying Chen, Chi Gui, Anningzhe Gao +4
The field of medical diagnosis has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these model…
LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them
Wenya Xie, Qingying Xiao, Yu Zheng +7
The recent success of Large Language Models (LLMs) has had a significant impact on the healthcare field, providing patients with medical advice, diagnostic information, and more. H…
LLMs Could Autonomously Learn Without External Supervision
Ke Ji, Junying Chen, Anningzhe Gao +3
In the quest for super-human performance, Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives-a process tha…
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Junying Chen, Chi Gui, Ruyi Ouyang +10
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multi…
Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue
Jiahui Xu, Feng Jiang, Anningzhe Gao +2
In dialogue systems, discourse plays a crucial role in managing conversational focus and coordinating interactions. It consists of two key structures: rhetorical structure and topi…
LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages
Xuhan Huang, Qingning Shen, Yan Hu +2
Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, yet their proficiency in mathematical reasoning remains a key ch…
Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Xidong Wang, Nuo Chen, Junyin Chen +9
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in are…
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
Wentao Ge, Shunian Chen, Guiming Hardy Chen +14
Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries…
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
Junying Chen, Xidong Wang, Ke Ji +11
Adapting a language model into a specific domain, a.k.a `domain adaption', is a common practice when specialized knowledge, e.g. medicine, is not encapsulated in a general language…
OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning
Fei Yu, Anningzhe Gao, Benyou Wang
Large language models (LLMs) often struggle with maintaining accuracy throughout multiple multiple reasoning steps, especially in mathematical reasoning where an error in earlier s…