Showing cs.CLShow all
3 papers · 1 filter
cs.CL2025
DomainCQA: Crafting Knowledge-Intensive QA from Domain-Specific Charts
Yujing Lu, Ling Zhong, Jing Yang +5
Chart Question Answering (CQA) evaluates Multimodal Large Language Models (MLLMs) on visual understanding and reasoning over chart data. However, existing benchmarks mostly test su…
cs.CL2024
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
Xingyun Hong, Yan Shao, Zhilin Wang +2
The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize e…
cs.CL2024
Large Language Models Understand Layout
Weiming Li, Manni Duan, Dong An +1
Large language models (LLMs) demonstrate extraordinary abilities in a wide range of natural language processing (NLP) tasks. In this paper, we show that, beyond text understanding…