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Hongfei Lin

15 papers hereh-index 9248 citations36 works total

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

author position
  • middle author4
  • last author11

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.IR6
same name
  • Hongfei Lin — 16 papers, h 5
  • Hongfei Lin — 10 papers, h 43
  • Hongfei Lin — 6 papers, h 18
  • Hongfei Lin — 5 papers, h 1
  • Hongfei Lin — 3 papers, h 7
  • Hongfei Lin — 3 papers, h 1

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 citedDisentangling ID and Modality Effects for Session-based Recommendation

31 citations · 92 across the 15 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos

Junyu Lu, Deyi Ji, Liqun Liu +9

Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification an…

cs.CL2026

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes

Weiming Wang, Junyu Lu, Han Wang +5

Research on harmful meme detection has garnered significant attention, resulting in the development of numerous datasets and methods. However, progress in detecting Chinese harmful…

cs.CL2026

Harder to Defend: Towards Chinese Toxicity Attacks via Implicit Enhancement and Obfuscation Rewriting

Jingyi Kang, Junyu Lu, Bo Xu +4

Large language models (LLMs) require robust toxicity evaluation beyond explicit wording. This setting remains underexplored in Chinese, where toxicity may combine semantic indirect…

cs.CL2026

Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis

Junyu Lu, Deyi Ji, Xuanyi Liu +5

Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single supervision signal. This par…

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