5 citations · 8 across the 8 of their papers we have counts for
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Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
Minsang Kim, Seung Jun Baek
Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain-of-Thoughts for reasoning tasks. KD m…
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…
Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods
Min Sik Oh, Min Sang Kim
Persona and Knowledge dual context open-domain chat is a novel dialogue generation task introduced recently. While Persona and Knowledge is each interesting context of open-domain…