25 citations · 95 across the 16 of their papers we have counts for
14 papers · 1 filter
FutureBridge: Token Selection Beyond Local Preference in Collaborative Decoding
Quanquan Li, Hongbo Zhang, Yihe Chi +9
Token-level collaboration allows a large language model (LLM) to assist a small language model (SLM) when their predictions diverge. Existing methods either use LLM-generated inter…
PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents
Yubo Hou, Jingwei Song, Hongbo Zhang +4
Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing agent memory methods emphasize…
Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values
Hongbo Zhang, Han Cui, Guangsheng Bao +3
We introduce Direct Value Optimization (DVO), an innovative reinforcement learning framework for enhancing large language models in complex reasoning tasks. Unlike traditional meth…
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
Lei Shi, Zhimeng Liu, Yi Yang +13
The extraction of Metal-Organic Frameworks (MOFs) synthesis route from literature has been crucial for the logical MOFs design with desirable functionality. The recent advent of la…
How Likely Do LLMs with CoT Mimic Human Reasoning?
Guangsheng Bao, Hongbo Zhang, Cunxiang Wang +2
Chain-of-thought emerges as a promising technique for eliciting reasoning capabilities from Large Language Models (LLMs). However, it does not always improve task performance or ac…
Co-training and Co-distillation for Quality Improvement and Compression of Language Models
Hayeon Lee, Rui Hou, Jongpil Kim +4
Knowledge Distillation (KD) compresses computationally expensive pre-trained language models (PLMs) by transferring their knowledge to smaller models, allowing their use in resourc…
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…
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs)…
HuatuoGPT, towards Taming Language Model to Be a Doctor
Hongbo Zhang, Junying Chen, Feng Jiang +10
In this paper, we present HuatuoGPT, a large language model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both \textit{distilled data from ChatGPT} an…
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
Hongbo Zhang, Xiang Wan, Benyou Wang
Pre-trained language models (PLMs) were considered to be able to store relational knowledge present in the training data. However, some relational knowledge seems to be discarded u…
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
Hongbo Zhang, Chen Tang, Tyler Loakman +3
Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), resul…
Phoenix: Democratizing ChatGPT across Languages
Zhihong Chen, Feng Jiang, Junying Chen +11
This paper presents our efforts to democratize ChatGPT across language. We release a large language model "Phoenix", achieving competitive performance among open-source English and…
Natural Language Reasoning, A Survey
Fei Yu, Hongbo Zhang, Prayag Tiwari +1
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provi…
Terminology-aware Medical Dialogue Generation
Chen Tang, Hongbo Zhang, Tyler Loakman +2
Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires…