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Jia Deng

10 papers hereh-index 8809 citations13 works total

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

author position
  • first author3
  • middle author7

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.AI1
same name
  • Jia Deng — 56 papers, h 58
  • Jia Deng — 10 papers, h 4
  • Jia Deng — 9 papers, h 4
  • Jia Deng — 6 papers, h 6
  • Jia Deng — 6 papers, h 4
  • Jia Deng — 5 papers, h 4

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 citedLlemma: An Open Language Model For Mathematics

15 citations · 17 across the 10 of their papers we have counts for

collaborators
Showing 2024 · cs.CLShow all

4 papers · 2 filters

cs.CL2024

Enhancing LLM Reasoning with Reward-guided Tree Search

Jinhao Jiang, Zhipeng Chen, Yingqian Min +12

Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By alloc…

cs.CL2024

YuLan-Mini: An Open Data-efficient Language Model

Yiwen Hu, Huatong Song, Jia Deng +8

Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper p…

cs.CL2024

Neuron-based Personality Trait Induction in Large Language Models

Jia Deng, Tianyi Tang, Yanbin Yin +3

Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-p…

cs.CL2024

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation

Sirui Xia, Xintao Wang, Jiaqing Liang +5

Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG s…

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