60 citations · 63 across the 7 of their papers we have counts for
5 papers · 1 filter
How Do Large Language Models Learn Concepts During Continual Pre-Training?
Barry Menglong Yao, Sha Li, Yunzhi Yao +4
Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large lan…
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Adithya Kulkarni, Fatimah Alotaibi, Xinyue Zeng +7
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmen…
Error-driven Data-efficient Large Multimodal Model Tuning
Barry Menglong Yao, Qifan Wang, Lifu Huang
Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks. However, fine-tuning still remains essential to achieve satisfactory pe…
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
Barry Menglong Yao, Sijia Wang, Yu Chen +5
We propose attribute-aware multimodal entity linking, where the input consists of a mention described with a text paragraph and images, and the goal is to predict the corresponding…
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
Barry Menglong Yao, Aditya Shah, Lichao Sun +2
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and…