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researcher

Yinghao Li

Georgia Institute of Technology

18 papers hereh-index 14581 citations24 works total

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

author position
  • first author9
  • middle author9

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL13
  • cs.LG2
  • cs.AI1
  • cs.IR1
  • physics.chem-ph1
affiliations
  • Georgia Institute of Technology
Homepage
same name
  • Yinghao Li — 9 papers, h 7
  • Yinghao Li — 3 papers, h 2
  • Yinghao Li — 2 papers, h 2
  • Yinghao Li — 1 paper
  • Yinghao Li — 1 paper, h 2
  • Yinghao Li — 1 paper, h 9

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
20202025
most citedSTEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

47 citations · 170 across the 17 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.AI2025★ 1 cited

MSQA: Benchmarking LLMs on Graduate-Level Materials Science Reasoning and Knowledge

Jerry Junyang Cheung, Shiyao Shen, Yuchen Zhuang +3

Despite recent advances in large language models (LLMs) for materials science, there is a lack of benchmarks for evaluating their domain-specific knowledge and complex reasoning ab…

cs.LG2025

MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering

Rushi Qiang, Yuchen Zhuang, Yinghao Li +8

We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents in iterative machine…

cs.CL2025

Language Model Uncertainty Quantification with Attention Chain

Yinghao Li, Rushi Qiang, Lama Moukheiber +1

Accurately quantifying a large language model's (LLM) predictive uncertainty is crucial for judging the reliability of its answers. While most existing research focuses on short, d…

cs.CL2025

Ensembles of Low-Rank Expert Adapters

Yinghao Li, Vianne Gao, Chao Zhang +1

The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions…

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