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Lingpeng Kong

43 papers hereh-index 417.7k citations94 works total

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

author position
  • first author3
  • middle author29
  • last author9

Across the 41 of 43 papers where every author was matched, so the position is known.

fields
  • cs.CL35
  • cs.CV4
  • cs.LG4
same name
  • Lingpeng Kong — 19 papers, h 11
  • Lingpeng Kong — 17 papers, h 11
  • Lingpeng Kong — 12 papers, h 7
  • Lingpeng Kong — 10 papers, h 7
  • Lingpeng Kong — 10 papers, h 5
  • Lingpeng Kong — 8 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
20152023
most citedLearning and Evaluating General Linguistic Intelligence

157 citations · 591 across the 32 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 14 cited

M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning

Lei Li, Yuwei Yin, Shicheng Li +9

Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress i…

cs.CV2023★ 2 cited

TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models

Yuwei Yin, Jean Kaddour, Xiang Zhang +4

Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise inje…

cs.CV2023

Fine-grained Audible Video Description

Xuyang Shen, Dong Li, Jinxing Zhou +9

We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audibl…

cs.CV2022★ 38 cited

Language Models Can See: Plugging Visual Controls in Text Generation

Yixuan Su, Tian Lan, Yahui Liu +5

Generative language models (LMs) such as GPT-2/3 can be prompted to generate text with remarkable quality. While they are designed for text-prompted generation, it remains an open…

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