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Heng Huang

11 papers hereh-index 6119 citations13 works total

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

author position
  • middle author5
  • last author6

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

fields
  • cs.CV5
  • cs.LG4
  • cs.AI1
  • cs.CL1
same name
  • Heng Huang — 61 papers, h 42
  • Heng Huang — 24 papers, h 63
  • Heng Huang — 16 papers, h 8
  • Heng Huang — 16 papers, h 5
  • Heng Huang — 13 papers, h 11
  • Heng Huang — 12 papers, h 10

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
20232026
most citedFrom Pixels to Prose: A Large Dataset of Dense Image Captions

1 citations · 2 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation

Reza Shirkavand, Xiaokai Wei, Chen Wang +3

While collaborative filtering delivers predictive accuracy and efficiency, and Large Language Models (LLMs) enable expressive and generalizable reasoning, modern recommendation sys…

cs.LG2025

Cost-Aware Contrastive Routing for LLMs

Reza Shirkavand, Shangqian Gao, Peiran Yu +1

We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive mode…

cs.CV2025

ARGUS: Hallucination and Omission Evaluation in Video-LLMs

Ruchit Rawal, Reza Shirkavand, Heng Huang +2

Video large language models have not yet been widely deployed, largely due to their tendency to hallucinate. Typical benchmarks for Video-LLMs rely simply on multiple-choice questi…

cs.LG2025

Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training

Reza Shirkavand, Peiran Yu, Qi He +1

Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-…

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