3 citations · 8 across the 18 of their papers we have counts for
5 papers · 1 filter
Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data
Suhwan Park, Hoyoung Lee, Zhangyang Wang +5
Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance. Simple persona prompts rarely specify…
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…
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…