2 citations · 8 across the 13 of their papers we have counts for
13 papers
Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion
Jacob Hansen, Wei Lin, Junmo Kang +6
Visual Instruction Tuning (VisIT) data, commonly available as human-assistant conversations with images interleaved in the human turns, are currently the most widespread vehicle fo…
Balancing the Budget: Understanding Trade-offs Between Supervised and Preference-Based Finetuning
Mohit Raghavendra, Junmo Kang, Alan Ritter
Post-training of Large Language Models often involves a pipeline of Supervised Finetuning (SFT) followed by Preference Finetuning (PFT) using methods like Direct Preference Optimiz…
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs
Jungsoo Park, Junmo Kang, Gabriel Stanovsky +1
The surge of LLM studies makes synthesizing their findings challenging. Analysis of experimental results from literature can uncover important trends across studies, but the time-c…
MATE: Meet At The Embedding -- Connecting Images with Long Texts
Young Kyun Jang, Junmo Kang, Yong Jae Lee +1
While advancements in Vision Language Models (VLMs) have significantly improved the alignment of visual and textual data, these models primarily focus on aligning images with short…
Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts
Junmo Kang, Leonid Karlinsky, Hongyin Luo +7
We present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts…
Self-Specialization: Uncovering Latent Expertise within Large Language Models
Junmo Kang, Hongyin Luo, Yada Zhu +6
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from…
Schema-Driven Information Extraction from Heterogeneous Tables
Fan Bai, Junmo Kang, Gabriel Stanovsky +3
In this paper, we explore the question of whether large language models can support cost-efficient information extraction from tables. We introduce schema-driven information extrac…
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Junmo Kang, Wei Xu, Alan Ritter
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to…
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Giwon Hong, Jeonghwan Kim, Junmo Kang +2
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more chal…
Maximizing Efficiency of Language Model Pre-training for Learning Representation
Junmo Kang, Suwon Shin, Jeonghwan Kim +2
Pre-trained language models in the past years have shown exponential growth in model parameters and compute time. ELECTRA is a novel approach for improving the compute efficiency o…
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval
Kyoung-Rok Jang, Junmo Kang, Giwon Hong +4
The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models' dense representatio…
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
Junmo Kang, Jeonghwan Kim, Suwon Shin +1
Tag recommendation relies on either a ranking function for top- tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicti…
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
Junmo Kang, Haritz Puerto San Roman, Sung-Hyon Myaeng
Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generatin…