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Jie Hu

5 papers hereh-index 495 citations7 works total

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

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.LG1
same name
  • Jie Hu — 18 papers, h 18
  • Jie Hu — 16 papers, h 10
  • Jie Hu — 14 papers, h 7
  • Jie Hu — 11 papers, h 4
  • Jie Hu — 10 papers, h 5
  • Jie Hu — 10 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

most citedControllable Text Generation for Large Language Models: A Survey

5 citations · 5 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Token-level Accept or Reject: A Micro Alignment Approach for Large Language Models

Yang Zhang, Yu Yu, Bo Tang +8

With the rapid development of Large Language Models (LLMs), aligning these models with human preferences and values is critical to ensuring ethical and safe applications. However,…

cs.CL2024

Xinyu: An Efficient LLM-based System for Commentary Generation

Yiquan Wu, Bo Tang, Chenyang Xi +13

Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skille…

cs.CL2024★ 5 cited

Controllable Text Generation for Large Language Models: A Survey

Xun Liang, Hanyu Wang, Yezhaohui Wang +8

In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly…

cs.CL2024

Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy

Yu Zhu, Chuxiong Sun, Wenfei Yang +8

Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.