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20172026
most citedInstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

409 citations · 485 across the 34 of their papers we have counts for

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7 papers · 1 filter

cs.CL2024

Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels

Chaoqun Liu, Qin Chao, Wenxuan Zhang +4

Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by…

cs.CL2024

Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding

Yidan Sun, Jianfei Yu, Boyang Li

Story video-text alignment, a core task in computational story understanding, aims to align video clips with corresponding sentences in their descriptions. However, progress on the…

cs.CL2024

Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA

Miaoyu Li, Haoxin Li, Zilin Du +1

Knowledge-based Visual Question-answering (K-VQA) often requires the use of background knowledge beyond the image. However, we discover that a single knowledge generation strategy…

cs.CL2023

Event Causality Is Key to Computational Story Understanding

Yidan Sun, Qin Chao, Boyang Li

Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding. However, machine learning systems for story understandin…

cs.CL2023

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

Tong Zhang, Yong Liu, Boyang Li +5

With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Ses…

cs.CL2022

Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation

Xu Guo, Boyang Li, Han Yu

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP t…