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Xiang Li

4 papers hereh-index 14 citations4 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.CV1
  • cs.HC1
same name
  • Xiang Li — 47 papers, h 21
  • Xiang Li — 45 papers, h 32
  • Xiang Li — 27 papers, h 32
  • Xiang Li — 26 papers, h 58
  • Xiang Li — 18 papers, h 17
  • Xiang Li — 17 papers, h 11

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

collaborators

4 papers

cs.CL2025

Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity

Ping Chen, Xiang Liu, Zhaoxiang Liu +7

With the rapid advancement of large language models (LLMs), natural language processing (NLP) has achieved remarkable progress. Nonetheless, significant challenges remain in handli…

cs.CV2025

Hierarchical Deep Fusion Framework for Multi-dimensional Facial Forgery Detection -- The 2024 Global Deepfake Image Detection Challenge

Kohou Wang, Huan Hu, Xiang Liu +4

The proliferation of sophisticated deepfake technology poses significant challenges to digital security and authenticity. Detecting these forgeries, especially across a wide spectr…

cs.HC2025

iLearnRobot: An Interactive Learning-Based Multi-Modal Robot with Continuous Improvement

Kohou Wang, ZhaoXiang Liu, Lin Bai +5

It is crucial that robots' performance can be improved after deployment, as they are inherently likely to encounter novel scenarios never seen before. This paper presents an innova…

cs.CL2025

SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models

Xiang Liu, Zhaoxiang Liu, Peng Wang +4

When using supervised fine-tuning (SFT) to adapt large language models (LLMs) to specific domains, a significant challenge arises: should we use the entire SFT dataset for fine-tun…

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