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Lingpeng Kong

10 papers hereh-index 583 citations13 works total

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

author position
  • middle author10

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

fields
  • cs.LG5
  • cs.CL4
  • cs.AI1
same name
  • Lingpeng Kong — 17 papers, h 11
  • Lingpeng Kong — 14 papers, h 11
  • Lingpeng Kong — 12 papers, h 7
  • Lingpeng Kong — 10 papers, h 7
  • Lingpeng Kong — 7 papers, h 5
  • Lingpeng Kong — 6 papers, h 6

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
collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models

Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen +7

High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 millio…

cs.CL2025

How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models

Jiyue Jiang, Pengan Chen, Liheng Chen +5

The rapid evolution of large language models (LLMs) has transformed the competitive landscape in natural language processing (NLP), particularly for English and other data-rich lan…

cs.CL2024

Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting

Jiyue Jiang, Liheng Chen, Sheng Wang +3

Existing dialogue data augmentation (DA) techniques predominantly focus on augmenting utterance-level dialogues, which makes it difficult to take dialogue contextual information in…

cs.CL2024

LoRA Meets Dropout under a Unified Framework

Sheng Wang, Liheng Chen, Jiyue Jiang +3

With the remarkable capabilities, large language models (LLMs) have emerged as essential elements in numerous NLP applications, while parameter-efficient finetuning, especially LoR…

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