10 citations · 11 across the 6 of their papers we have counts for
6 papers
Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring
Weixin Guan, Liang Li, Jiapeng Liu +6
Large Reasoning Language Models (LRLMs) demonstrate impressive capabilities on complex tasks by utilizing long Chain-of-Thought reasoning. However, they are prone to overthinking,…
EXaMCaP: Subset Selection with Entropy Gain Maximization for Probing Capability Gains of Large Chart Understanding Training Sets
Jiapeng Liu, Liang Li, Bing Li +5
Recent works focus on synthesizing Chart Understanding (ChartU) training sets to inject advanced chart knowledge into Multimodal Large Language Models (MLLMs), where the sufficienc…
Separate and Locate: Rethink the Text in Text-based Visual Question Answering
Chengyang Fang, Jiangnan Li, Liang Li +2
Text-based Visual Question Answering (TextVQA) aims at answering questions about the text in images. Most works in this field focus on designing network structures or pre-training…
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
Liang Li, Ruiying Geng, Chengyang Fang +6
There are three problems existing in the popular data-to-text datasets. First, the large-scale datasets either contain noise or lack real application scenarios. Second, the dataset…
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Liang Li, Ruiying Geng, Chengyang Fang +4
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation pr…
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Chengyang Fang, Gangyan Zeng, Yu Zhou +4
Texts in scene images convey critical information for scene understanding and reasoning. The abilities of reading and reasoning matter for the model in the text-based visual questi…