110 citations · 271 across the 36 of their papers we have counts for
8 papers · 1 filter
Distributional Alignment as a Criterion for Designing Task Vectors in In-Context Learning
Jihoon Kwon, Jiwon Choi, Jy-yong Sohn
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks through demonstrations, yet it suffers from escalating inference costs as context length increas…
Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
Chungpa Lee, Jy-yong Sohn, Kangwook Lee
Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations. In practice, such models ar…
Linq-Embed-Mistral Technical Report
Chanyeol Choi, Junseong Kim, Seolhwa Lee +5
This report explores the enhancement of text retrieval performance using advanced data refinement techniques. We develop Linq-Embed-Mistral\footnote{\url{https://huggingface.co/Lin…
Improving Multi-lingual Alignment Through Soft Contrastive Learning
Minsu Park, Seyeon Choi, Chanyeol Choi +2
Making decent multi-lingual sentence representations is critical to achieve high performances in cross-lingual downstream tasks. In this work, we propose a novel method to align mu…
ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification
Sehee Lim, Yejin Kim, Chi-Hyun Choi +2
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions fr…
Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses
Juyeon Kim, Jeongeun Lee, Yoonho Chang +3
Mitigating hallucination issues is a key challenge that must be overcome to reliably deploy large language models (LLMs) in real-world scenarios. Recently, various methods have bee…
Can Separators Improve Chain-of-Thought Prompting?
Yoonjeong Park, Hyunjin Kim, Chanyeol Choi +2
Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs). The basic idea of CoT is to let LLMs bre…
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Tuan Dinh, Jy-yong Sohn, Shashank Rajput +5
Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupe…