2 citations · 3 across the 8 of their papers we have counts for
8 papers
Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?
Eunki Kim, Na Min An, Wan Ju Kang +3
Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world…
Learning to Insert [PAUSE] Tokens for Better Reasoning
Eunki Kim, Sangryul Kim, James Thorne
To enhance reasoning capabilities, previous works have explored incorporating special-purpose tokens into the training process. These strategies strengthen the learning mechanism o…
From Evidence to Belief: A Bayesian Epistemology Approach to Language Models
Minsu Kim, Sangryul Kim, James Thorne
This paper investigates the knowledge of language models from the perspective of Bayesian epistemology. We explore how language models adjust their confidence and responses when pr…
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions
Wan Ju Kang, Eunki Kim, Na Min An +4
Often, the needs and visual abilities differ between the annotator group and the end user group. Generating detailed diagram descriptions for blind and low-vision (BLV) users is on…
How Blind and Low-Vision Individuals Prefer Large Vision-Language Model-Generated Scene Descriptions
Na Min An, Eunki Kim, Wan Ju Kang +3
For individuals with blindness or low vision (BLV), navigating complex environments can pose serious risks. Large Vision-Language Models (LVLMs) show promise for generating scene d…
Context Filtering with Reward Modeling in Question Answering
Sangryul Kim, James Thorne
Question Answering (QA) in NLP is the task of finding answers to a query within a relevant context retrieved by a retrieval system. Yet, the mix of relevant and irrelevant informat…
Re3val: Reinforced and Reranked Generative Retrieval
EuiYul Song, Sangryul Kim, Haeju Lee +2
Generative retrieval models encode pointers to information in a corpus as an index within the model's parameters. These models serve as part of a larger pipeline, where retrieved i…
eXplainable Bayesian Multi-Perspective Generative Retrieval
EuiYul Song, Philhoon Oh, Sangryul Kim +1
Modern deterministic retrieval pipelines prioritize achieving state-of-the-art performance but often lack interpretability in decision-making. These models face challenges in asses…