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Mark Chen

14 papers hereh-index 81.1k citations37 works total

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

author position
  • middle author2

Across the 2 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG2
  • cs.AI1
  • cs.CV1
same name
  • Mark Chen — 24 papers, h 16
  • Mark Chen — 3 papers, h 2
  • Mark Chen — 3 papers, h 1
  • Mark Chen — 2 papers, h 2
  • Mark Chen — 2 papers, h 4
  • Mark Chen — 1 paper

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

18 citations · 33 across the 14 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

4 papers · 2 filters

cs.CL2026

The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline

Elle

Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language modeling pipeline remains unclear…

cs.CL2026

TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models

Jinho Choo, JunSeung Lee, Jimyeong Kim +3

Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known…

cs.CL2026

Adam's Law: Textual Frequency Law on Large Language Models

Hongyuan Adam Lu, Z. L., Victor Wei +5

While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…

cs.CL2026★ 18 cited

OpenAI GPT-5 System Card

Aaditya Singh, Adam Fry, Adam Perelman +483

This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…

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