3 citations · 5 across the 9 of their papers we have counts for
9 papers
Training Language Models via Neural Cellular Automata
Dan Lee, Seungwook Han, Akarsh Kumar +1
Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: hig…
One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning
Jieun Han, Daniel Lee, Haneul Yoo +5
Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose…
DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models
Sunghee Jung, Donghun Lee, Shinbok Lee +7
Tool-Augmented Larage Language Models (TA-LLMs) have shown promise in real-world applications, but face challenges in handling incomplete queries and out-of-scope requests. While e…
ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices
Aneesh Vathul, Daniel Lee, Sheryl Chen +1
Large Language Models (LLMs) have demonstrated impressive capabilities on a broad array of NLP tasks, but their tendency to produce hallucinations$\unicode{x2013}$plausible-soundin…
Kanana: Compute-efficient Bilingual Language Models
Kanana LLM Team, Yunju Bak, Hojin Lee +26
We introduce Kanana, a series of bilingual language models that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana…
FunctionChat-Bench: Comprehensive Evaluation of Language Models' Generative Capabilities in Korean Tool-use Dialogs
Shinbok Lee, Gaeun Seo, Daniel Lee +3
This study investigates language models' generative capabilities in tool-use dialogs. We categorize the models' outputs in tool-use dialogs into four distinct types: Tool Call, Ans…
Investigating Sensitive Directions in GPT-2: An Improved Baseline and Comparative Analysis of SAEs
Daniel J. Lee, Stefan Heimersheim
Sensitive directions experiments attempt to understand the computational features of Language Models (LMs) by measuring how much the next token prediction probabilities change by p…
Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs
Yifan Wang, David Stevens, Pranay Shah +10
The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, lab…
Answering Chinese Elementary School Social Study Multiple Choice Questions
Daniel Lee, Chao-Chun Liang, Keh-Yih Su
We present a novel approach to answer the Chinese elementary school Social Study Multiple Choice questions. Although BERT has demonstrated excellent performance on Reading Comprehe…