4 papers
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…
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…