3 citations · 5 across the 12 of their papers we have counts for
14 papers
FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents
Hoyoung Lee, Suyeol Yun, Jack Haverty +17
Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on…
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
Jihoon Kwon, Lawrence Liu, Daekyung Park +15
When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, web traffic, and prediction ma…
Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders
Chungpa Lee, Jihoon Kwon, Kyle Min +1
Vision-language models map images and text into a joint embedding space. However, these embeddings often entangle multiple semantic features, which limits their interpretability an…
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…
Forecasting Future Language: Context Design for Mention Markets
Sumin Kim, Jihoon Kwon, Yoon Kim +9
Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilist…
LLM as a Risk Manager: LLM Semantic Filtering for Lead-Lag Trading in Prediction Markets
Sumin Kim, Minjae Kim, Jihoon Kwon +7
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains…
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon +8
Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making. Existing approaches define similarity through historical pri…
Enhancing Compositional Reasoning in CLIP via Reconstruction and Alignment of Text Descriptions
Jihoon Kwon, Kyle Min, Jy-yong Sohn
Despite recent advances, vision-language models trained with standard contrastive objectives still struggle with compositional reasoning -- the ability to understand structured rel…
From Text to Alpha: Can LLMs Track Evolving Signals in Corporate Disclosures?
Chanyeol Choi, Yoon Kim, Yu Yu +10
Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving…
FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Chanyeol Choi, Jihoon Kwon, Alejandro Lopez-Lira +8
Accurate information retrieval (IR) is critical in the financial domain, where investors must identify relevant information from large collections of documents. Traditional IR meth…
Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance
Chanyeol Choi, Alejandro Lopez-Lira, Yongjae Lee +8
Extracting structured and quantitative insights from unstructured financial filings is essential in investment research, yet remains time-consuming and resource-intensive. Conventi…
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
Chanyeol Choi, Jihoon Kwon, Jaeseon Ha +5
In the fast-paced financial domain, accurate and up-to-date information is critical to addressing ever-evolving market conditions. Retrieving this information correctly is essentia…
Bridging Language Models and Financial Analysis
Alejandro Lopez-Lira, Jihoon Kwon, Sangwoon Yoon +2
The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financia…
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…