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Michael Zhang

11 papers hereh-index 5682 citations14 works total

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

author position
  • sole author2
  • first author1
  • middle author5
  • last author3

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

fields
  • cs.CL5
  • cs.LG4
  • cs.SE2
same name
  • Michael Zhang — 16 papers, h 10
  • Michael Zhang — 6 papers, h 3
  • Michael Zhang — 6 papers, h 4
  • Michael Zhang — 3 papers, h 1
  • Michael Zhang — 3 papers, h 2
  • Michael 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 citedOpenAI GPT-5 System Card

17 citations · 17 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs

Andy Zeyi Liu, Michael Zhang, Ilana Greenberg +3

Steering large language models (LLMs) is usually done by either instruction prompting or activation steering. Prompting often gives strong control, but caches guidance tokens at ev…

cs.LG2025

Fast Exact Unlearning for In-Context Learning Data for LLMs

Andrei I. Muresanu, Anvith Thudi, Michael R. Zhang +1

Modern machine learning models are expensive to train, and there is a growing concern about the challenge of retroactively removing specific training data. Achieving exact unlearni…

cs.LG2024

Using Large Language Models for Hyperparameter Optimization

Michael R. Zhang, Nishkrit Desai, Juhan Bae +2

This paper explores the use of foundational large language models (LLMs) in hyperparameter optimization (HPO). Hyperparameters are critical in determining the effectiveness of mach…

cs.LG2024

Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries

Blair Yang, Fuyang Cui, Keiran Paster +4

The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We prop…

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