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Yitong Li

5 papers here

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

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.IR1
ORCID 0000-0002-6753-6227
same name
  • Yitong Li — 11 papers, h 17
  • Yitong Li — 7 papers, h 7
  • Yitong Li — 7 papers, h 7
  • Yitong Li — 6 papers, h 8
  • Yitong Li — 6 papers, h 10
  • Yitong Li — 6 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

activity
20142024
most citedHRank: A Path based Ranking Framework in Heterogeneous Information Network

2 citations · 4 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

Concise and Precise Context Compression for Tool-Using Language Models

Yang Xu, Yunlong Feng, Honglin Mu +9

Through reading the documentation in the context, tool-using language models can dynamically extend their capability using external tools. The cost is that we have to input lengthy…

cs.CL2024

Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation

Yiwei Li, Fei Mi, Yitong Li +4

Stochastic sampling strategies such as top-k and top-p have been widely used in dialogue generation task. However, as an open-domain chatting system, there will be two different co…

cs.CL2023★ 2 cited

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models

Jiaxu Zhao, Meng Fang, Zijing Shi +3

\textit{\textbf{\textcolor{red}{Warning}:} This paper contains content that may be offensive or upsetting.} Pretrained conversational agents have been exposed to safety issues, exh…

cs.CL2016

Learning Robust Representations of Text

Yitong Li, Trevor Cohn, Timothy Baldwin

Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present…

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