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Li Shen

4 papers here

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

author position
  • last author4

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

fields
  • cs.LG3
  • cs.CL1
same name
  • Li Shen — 45 papers
  • Li Shen — 23 papers
  • Li Shen — 16 papers, h 29
  • Li Shen — 10 papers
  • Li Shen — 7 papers, h 15
  • Li Shen — 7 papers

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

most citedICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning

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

collaborators

4 papers

cs.LG2025

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach

Jiancong Xiao, Bojian Hou, Zhanliang Wang +4

One of the key technologies for the success of Large Language Models (LLMs) is preference alignment. However, a notable side effect of preference alignment is poor calibration: whi…

cs.LG2025★ 1 cited

ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning

Ruochen Jin, Boning Tong, Shu Yang +2

Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a…

cs.LG2025

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

Jia Xu, Tianyi Wei, Bojian Hou +7

We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between B…

cs.CL2024

SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text

Weiqing He, Bojian Hou, Tianqi Shang +3

The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techn…

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