35 citations · 104 across the 27 of their papers we have counts for
8 papers · 1 filter
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
Fuxiao Liu, Xiaoyang Wang, Wenlin Yao +5
With the rapid development of large language models (LLMs) and their integration into large multimodal models (LMMs), there has been impressive progress in zero-shot completion of…
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
Xuansheng Wu, Wenlin Yao, Jianshu Chen +4
Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how…
A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation
Neeraj Varshney, Wenlin Yao, Hongming Zhang +2
Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically…
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
Jiaao Chen, Xiaoman Pan, Dian Yu +4
We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by comb…
Thrust: Adaptively Propels Large Language Models with External Knowledge
Xinran Zhao, Hongming Zhang, Xiaoman Pan +3
Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, maki…
PIVOINE: Instruction Tuning for Open-world Information Extraction
Keming Lu, Xiaoman Pan, Kaiqiang Song +3
We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional…
CEO: Corpus-based Open-Domain Event Ontology Induction
Nan Xu, Hongming Zhang, Jianshu Chen
Existing event-centric NLP models often only apply to the pre-defined ontology, which significantly restricts their generalization capabilities. This paper presents CEO, a novel Co…
Learning Language Representations with Logical Inductive Bias
Jianshu Chen
Transformer architectures have achieved great success in solving natural language tasks, which learn strong language representations from large-scale unlabeled texts. In this paper…