6 citations · 10 across the 6 of their papers we have counts for
7 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…
Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding
Xuanming Zhang, Sining Zhoubian, Yuxuan Chen +8
Autoregressive generation in large language models (LLMs) conventionally decodes from the final layer, assuming that deeper representations yield more reliable next-token predictio…
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…
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…
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…
Rock Classification Based on Residual Networks
Sining Zhoubian, Yuyang Wang, Zhihuan Jiang
Rock Classification is an essential geological problem since it provides important formation information. However, exploration on this problem using convolutional neural networks i…
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…