4 citations · 5 across the 6 of their papers we have counts for
6 papers
ChartCap: Mitigating Hallucination of Dense Chart Captioning
Junyoung Lim, Jaewoo Ahn, Gunhee Kim
Generating accurate, informative, and hallucination-free captions for charts remains challenging for vision language models, primarily due to the lack of large-scale, high-quality…
LPOI: Listwise Preference Optimization for Vision Language Models
Fatemeh Pesaran Zadeh, Yoojin Oh, Gunhee Kim
Aligning large VLMs with human preferences is a challenging task, as methods like RLHF and DPO often overfit to textual information or exacerbate hallucinations. Although augmentin…
Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type
Seokwon Song, Taehyun Lee, Jaewoo Ahn +2
Conceptual combination is a cognitive process that merges basic concepts, enabling the creation of complex expressions. During this process, the properties of combination (e.g., th…
Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback
Fatemeh Pesaran Zadeh, Juyeon Kim, Jin-Hwa Kim +1
Large language models (LLMs) have demonstrated strong capabilities across various language tasks, notably through instruction-tuning methods. However, LLMs face challenges in visua…
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models
Jaewoo Ahn, Taehyun Lee, Junyoung Lim +4
While Large Language Models (LLMs) can serve as agents to simulate human behaviors (i.e., role-playing agents), we emphasize the importance of point-in-time role-playing. This situ…
Who Wrote this Code? Watermarking for Code Generation
Taehyun Lee, Seokhee Hong, Jaewoo Ahn +5
Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are bein…