61 citations · 82 across the 14 of their papers we have counts for
15 papers
Memory for Large Language Models
Sining Zhoubian, Dan Zhang, Evgeny Kharlamov +1
Memory has evolved into a foundational architectural dimension in large language models (LLMs), shifting from an implicit byproduct of computation to a spectrum of explicit, contro…
RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism
Mengyang Sun, Maochuan Dou, Tao Feng +5
While Large Language Models (LLMs) are commonly fine-tuned to handle domain-specific tasks before being applied to vertical applications, adapting them to complex scenarios with di…
TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
Dan Zhang, Min Cai, Jonathan Light +3
Reward models are central to both reinforcement learning (RL) with language models and inference-time verification. However, existing reward models often lack temporal consistency,…
ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search
Sining Zhoubian, Dan Zhang, Jie Tang
With respect to improving the reasoning accuracy of LLMs, the representative reinforcement learning (RL) method - Group Relative Policy Optimization (GRPO) - has achieved critical…
A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
Mengyang Sun, Yihao Wang, Tao Feng +3
In order to streamline the fine-tuning of foundation models, Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domai…
DataSciBench: An LLM Agent Benchmark for Data Science
Dan Zhang, Sining Zhoubian, Min Cai +7
This paper presents DataSciBench, a comprehensive benchmark for evaluating Large Language Model (LLM) capabilities in data science. Recent related benchmarks have primarily focused…
Parameter-Efficient Fine-Tuning for Foundation Models
Dan Zhang, Tao Feng, Lilong Xue +3
This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs). PEFT, a cost-effective fine-tuning technique, minimizes p…
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think
Tao Feng, Wei Li, Didi Zhu +4
Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Optimizers such as SGD and Adam are commonly used for weight updates in continual learn…
BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Wei Wang, Dan Zhang, Tao Feng +2
Large Language Models (LLMs) are becoming increasingly powerful and capable of handling complex tasks, e.g., building single agents and multi-agent systems. Compared to single agen…
Multi-turn Response Selection with Commonsense-enhanced Language Models
Yuandong Wang, Xuhui Ren, Tong Chen +3
As a branch of advanced artificial intelligence, dialogue systems are prospering. Multi-turn response selection is a general research problem in dialogue systems. With the assistan…
LGB: Language Model and Graph Neural Network-Driven Social Bot Detection
Ming Zhou, Dan Zhang, Yuandong Wang +3
Malicious social bots achieve their malicious purposes by spreading misinformation and inciting social public opinion, seriously endangering social security, making their detection…
ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
Dan Zhang, Sining Zhoubian, Ziniu Hu +3
Recent methodologies in LLM self-training mostly rely on LLM generating responses and filtering those with correct output answers as training data. This approach often yields a low…
AutoRE: Document-Level Relation Extraction with Large Language Models
Lilong Xue, Dan Zhang, Yuxiao Dong +1
Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extractio…
RecDCL: Dual Contrastive Learning for Recommendation
Dan Zhang, Yangliao Geng, Wenwen Gong +6
Self-supervised learning (SSL) has recently achieved great success in mining the user-item interactions for collaborative filtering. As a major paradigm, contrastive learning (CL)…
SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models
Dan Zhang, Ziniu Hu, Sining Zhoubian +6
Large Language Models (LLMs) have shown promise in assisting scientific discovery. However, such applications are currently limited by LLMs' deficiencies in understanding intricate…