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Yisong Yue

12 papers hereh-index 8797 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author2

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI3
  • cs.CV2
  • cs.LG2
  • cs.SD1
same name
  • Yisong Yue — 82 papers, h 58
  • Yisong Yue — 30 papers, h 17
  • Yisong Yue — 12 papers, h 7
  • Yisong Yue — 12 papers, h 5
  • Yisong Yue — 8 papers, h 4
  • Yisong Yue — 6 papers, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search

6 citations · 17 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

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…

cs.CL2024★ 6 cited

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…

cs.CL2024

Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller

Min Cai, Yuchen Zhang, Shichang Zhang +5

We propose SelfControl, an inference-time model control method utilizing gradients to control the behavior of large language models (LLMs) without explicit human annotations. Given…

cs.CL2024★ 4 cited

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…

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