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Min Zeng

4 papers hereh-index 11 citations5 works total

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
same name
  • Min Zeng — 3 papers, h 2
  • Min Zeng — 2 papers, h 4
  • Min Zeng — 2 papers, h 1
  • Min Zeng — 2 papers, h 3
  • Min Zeng — 2 papers, h 4
  • Min Zeng — 2 papers, h 0

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
collaborators

4 papers

cs.CL2026

An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review

Zaifu Zhan, Yu Hou, Kai Yu +4

Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet communication needs through…

cs.CL2026

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

Zaifu Zhan, Min Zeng, Shuang Zhou +6

Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods:…

cs.CL2025

GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models

Min Zeng, Jingfei Sun, Xueyou Luo +4

In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tun…

cs.CL2024

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy

Min Zeng, Caiquan Liu, Shiqi Zhang +3

In recent years, the use of large language models (LLMs) for text classification has attracted widespread attention. Despite this, the classification accuracy of LLMs has not yet u…

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