5 citations · 17 across the 17 of their papers we have counts for
6 papers · 1 filter
How Well Do Large Language Models Truly Ground?
Hyunji Lee, Sejune Joo, Chaeeun Kim +4
To reduce issues like hallucinations and lack of control in Large Language Models (LLMs), a common method is to generate responses by grounding on external contexts given as input,…
KTRL+F: Knowledge-Augmented In-Document Search
Hanseok Oh, Haebin Shin, Miyoung Ko +2
We introduce a new problem KTRL+F, a knowledge-augmented in-document search task that necessitates real-time identification of all semantic targets within a document with the aware…
Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment
Yongrae Jo, Seongyun Lee, Aiden SJ Lee +3
Dense video captioning, a task of localizing meaningful moments and generating relevant captions for videos, often requires a large, expensive corpus of annotated video segments pa…
Local 3D Editing via 3D Distillation of CLIP Knowledge
Junha Hyung, Sungwon Hwang, Daejin Kim +2
3D content manipulation is an important computer vision task with many real-world applications (e.g., product design, cartoon generation, and 3D Avatar editing). Recently proposed…
Exploring the Practicality of Generative Retrieval on Dynamic Corpora
Chaeeun Kim, Soyoung Yoon, Hyunji Lee +3
Benchmarking the performance of information retrieval (IR) is mostly conducted with a fixed set of documents (static corpora). However, in realistic scenarios, this is rarely the c…
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Sohee Yang, Jonghyeon Kim, Joel Jang +3
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal pro…