110 citations · 262 across the 24 of their papers we have counts for
16 papers · 1 filter
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Keming Lu, Hongyi Yuan, Runji Lin +4
The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs…
Speculative Contrastive Decoding
Hongyi Yuan, Keming Lu, Fei Huang +2
Large language models~(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-opt…
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
Guanting Dong, Hongyi Yuan, Keming Lu +7
Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, code generation, and instruction following. These…
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Zheng Yuan, Hongyi Yuan, Chengpeng Li +5
Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, w…
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Keming Lu, Hongyi Yuan, Zheng Yuan +5
Foundation language models obtain the instruction-following ability through supervised fine-tuning (SFT). Diversity and complexity are considered critical factors of a successful S…
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Zheng Yuan, Hongyi Yuan, Chuanqi Tan +3
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions bet…
Revisiting Automatic Question Summarization Evaluation in the Biomedical Domain
Hongyi Yuan, Yaoyun Zhang, Fei Huang +1
Automatic evaluation metrics have been facilitating the rapid development of automatic summarization methods by providing instant and fair assessments of the quality of summaries.…
How well do Large Language Models perform in Arithmetic tasks?
Zheng Yuan, Hongyi Yuan, Chuanqi Tan +2
Large language models have emerged abilities including chain-of-thought to answer math word problems step by step. Solving math word problems not only requires abilities to disasse…
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Hongyi Yuan, Keming Lu, Zheng Yuan
Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED). EL models are trained on corpora labeled by a predefined KB. Howeve…
SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
Hongyi Yuan, Zheng Yuan, Chuanqi Tan +2
Diffusion model, a new generative modelling paradigm, has achieved great success in image, audio, and video generation. However, considering the discrete categorical nature of text…
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
Hongyi Yuan, Zheng Yuan, Chuanqi Tan +2
Language models with the Transformers structure have shown great performance in natural language processing. However, there still poses problems when fine-tuning pre-trained langua…
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Hongyi Yuan, Zheng Yuan, Sheng Yu
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform conc…
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
Sheng Yu, Zheng Yuan, Jun Xia +11
Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language process…
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Hongyi Yuan, Zheng Yuan, Ruyi Gan +3
Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific dow…