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researcher

Kam-Fai Wong

22 papers hereh-index 10310 citations36 works total

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

author position
  • middle author8
  • last author11

Across the 19 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.LG5
  • cs.CV3
  • cs.AI2
  • cs.MM2
  • cs.IR1
same name
  • Kam-Fai Wong — 34 papers, h 19
  • Kam-Fai Wong — 31 papers, h 40
  • Kam-Fai Wong — 10 papers, h 2
  • Kam-Fai Wong — 7 papers, h 5
  • Kam-Fai Wong — 6 papers, h 6
  • Kam-Fai Wong — 5 papers, h 5

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 citedOSPC: Detecting Harmful Memes with Large Language Model as a Catalyst

3 citations · 12 across the 19 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning

Yiming Du, Yifan Xiang, Bin Liang +3

Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early…

cs.CL2025

MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents

Yiming Du, Bingbing Wang, Yang He +7

Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities fo…

cs.CV2025

Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists

Bojia Zi, Penghui Ruan, Marco Chen +7

Recent advancements in video generation have spurred the development of video editing techniques, which can be divided into inversion-based and end-to-end methods. However, current…

cs.LG2025

PEARL: Towards Permutation-Resilient LLMs

Liang Chen, Li Shen, Yang Deng +3

The in-context learning (ICL) capability of large language models (LLMs) enables them to perform challenging tasks using provided demonstrations. However, ICL is highly sensitive t…

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