activity
20162023
most citedFILIP: Fine-grained Interactive Language-Image Pre-Training

206 citations · 624 across the 70 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CL2023★ 2 cited

Prompt-Based Length Controlled Generation with Reinforcement Learning

Renlong Jie, Xiaojun Meng, Lifeng Shang +2

Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of…

cs.CL2023★ 6 cited

AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models

Siheng Li, Cheng Yang, Yichun Yin +6

Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stym…

cs.CL2023★ 2 cited

NewsDialogues: Towards Proactive News Grounded Conversation

Siheng Li, Yichun Yin, Cheng Yang +7

Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce…

cs.CL2023

Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video

Zenan Xu, Xiaojun Meng, Yasheng Wang +4

Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our…

cs.CL2023

Enhancing Coherence of Extractive Summarization with Multitask Learning

Renlong Jie, Xiaojun Meng, Lifeng Shang +2

This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discrimi…

cs.CL2023★ 7 cited

PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing

Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14

The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…