5 citations · 6 across the 5 of their papers we have counts for
6 papers
MoEZip: Routing-Aware KV Cache Compression for Sparse Mixture-of-Experts LLMs
Minsang Kim, Seungjun Baek
Mixture-of-Experts (MoE) language models scale model capacity efficiently by activating a small subset of parameters per token. However, in long-context inference, memory remains a…
Syntriever: How to Train Your Retriever with Synthetic Data from LLMs
Minsang Kim, Seungjun Baek
LLMs have boosted progress in many AI applications. Recently, there were attempts to distill the vast knowledge of LLMs into information retrieval systems. Those distillation metho…
Exploring Large Language Models on Cross-Cultural Values in Connection with Training Methodology
Minsang Kim, Seungjun Baek
Large language models (LLMs) closely interact with humans, and thus need an intimate understanding of the cultural values of human society. In this paper, we explore how open-sourc…
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
Minsang Kim, Cheoneum Park, Seungjun Baek
Retrieval-augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the parametric knowledge of large lang…
Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy
Minsang Kim, Seungjun Baek
Compute-efficient training of language models has become an important issue. We consider data pruning for data-efficient training of LLMs. In this work, we consider a data pruning…
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction
Minsang Kim, Seungjun Baek
Learning positional information of nodes in a graph is important for link prediction tasks. We propose a representation of positional information using representative nodes called…