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Hejia Zhang

4 papers hereh-index 6511 citations11 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.LG1
  • cs.SE1
same name
  • Hejia Zhang — 9 papers, h 13
  • Hejia Zhang — 4 papers
  • Hejia Zhang — 4 papers

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 citedMulti-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following

5 citations · 9 across the 4 of their papers we have counts for

collaborators

4 papers

cs.SE2026★ 1 cited

The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes

Redacted by arXiv

This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…

cs.CL2025★ 1 cited

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following

Yun He, Wenzhe Li, Hejia Zhang +22

Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, a…

cs.CL2024★ 5 cited

Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following

Yun He, Di Jin, Chaoqi Wang +16

Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expe…

cs.LG2024★ 2 cited

The Perfect Blend: Redefining RLHF with Mixture of Judges

Tengyu Xu, Eryk Helenowski, Karthik Abinav Sankararaman +17

Reinforcement learning from human feedback (RLHF) has become the leading approach for fine-tuning large language models (LLM). However, RLHF has limitations in multi-task learning…

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