30 citations · 176 across the 66 of their papers we have counts for
10 papers · 1 filter
Anchors Aweigh! Sail for Optimal Unified Multi-Modal Representations
Minoh Jeong, Zae Myung Kim, Min Namgung +3
A unified representation space in multi-modal learning is essential for effectively integrating diverse data sources, such as text, images, and audio, to enhance efficiency and per…
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Minhwa Lee, Zae Myung Kim, Vivek Khetan +1
Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific…
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati +2
Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learni…
Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation
Karin de Langis, Ryan Koo, Dongyeop Kang
Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust). An open question is…
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long +4
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. Howe…
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering
Jihyung Kil, Farideh Tavazoee, Dongyeop Kang +1
Visual Question Answering (VQA) often involves diverse reasoning scenarios across Vision and Language (V&L). Most prior VQA studies, however, have merely focused on assessing the m…
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
Zae Myung Kim, Kwang Hee Lee, Preston Zhu +2
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of id…
Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback
Daechul Ahn, Yura Choi, Youngjae Yu +2
Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tu…
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das, Karin De Langis, Anna Martin-Boyle +14
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…
SelectLLM: Can LLMs Select Important Instructions to Annotate?
Ritik Sachin Parkar, Jaehyung Kim, Jong Inn Park +1
Instruction tuning benefits from large and diverse datasets; however, creating such datasets involves a high cost of human labeling. While synthetic datasets generated by large lan…