1 citations · 2 across the 5 of their papers we have counts for
6 papers
NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
Lanrui Wang, Mingyu Zheng, Hongyin Tang +5
Processing structured tabular data, particularly large and lengthy tables, constitutes a fundamental yet challenging task for large language models (LLMs). However, existing long-c…
A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation
Chenxu Yang, Zheng Lin, Chong Tian +6
Grounding external knowledge can enhance the factuality of responses in dialogue generation. However, excessive emphasis on it might result in the lack of engaging and diverse expr…
Think out Loud: Emotion Deducing Explanation in Dialogues
Jiangnan Li, Zheng Lin, Lanrui Wang +6
Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to re…
An Empirical Study of Instruction-tuning Large Language Models in Chinese
Qingyi Si, Tong Wang, Zheng Lin +3
The success of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI). Subsequently, the release of LLMs has sparked the open-sour…
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Chenxu Yang, Zheng Lin, Lanrui Wang +6
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context. However, the model often fails…
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
Qingyi Si, Yuanxin Liu, Zheng Lin +2
Despite the excellent performance of vision-language pre-trained models (VLPs) on conventional VQA task, they still suffer from two problems: First, VLPs tend to rely on language b…