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Haibo Zhang

12 papers hereh-index 438 citations16 works total

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

author position
  • middle author12

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

fields
  • cs.CL4
  • cs.LG4
  • cs.AI2
  • cs.CV1
  • cs.IR1
same name
  • Haibo Zhang — 6 papers, h 13
  • Haibo Zhang — 3 papers, h 1
  • Haibo Zhang — 2 papers, h 7
  • Haibo Zhang — 2 papers, h 6
  • Haibo Zhang — 2 papers, h 5
  • Haibo Zhang — 2 papers, h 7

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 citedMid-Training of Large Language Models: A Survey

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Decomposing and Steering Functional Metacognition in Large Language Models

Yanshi Li, Xueru Bai, Shuman Liu +2

Large language models (LLMs) increasingly exhibit behaviors suggesting awareness of their evaluation context, often adapting their reasoning strategies in benchmark settings. Prior…

cs.CL2025

Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddings

Pakorn Ueareeworakul, Shuman Liu, Jinghao Feng +7

As global e-commerce rapidly expands into emerging markets, the lack of high-quality semantic representations for low-resource languages has become a decisive bottleneck for retrie…

cs.CL2025★ 1 cited

Mid-Training of Large Language Models: A Survey

Kaixiang Mo, Yuxin Shi, Weiwei Weng +4

Large language models (LLMs) are typically developed through large-scale pre-training followed by task-specific fine-tuning. Recent advances highlight the importance of an intermed…

cs.CL2025

Optimal Transport-Based Token Weighting scheme for Enhanced Preference Optimization

Meng Li, Guangda Huzhang, Haibo Zhang +2

Direct Preference Optimization (DPO) has emerged as a promising framework for aligning Large Language Models (LLMs) with human preferences by directly optimizing the log-likelihood…

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