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20242026
most citedLLM-as-a-Judge & Reward Model: What They Can and Cannot Do

1 citations · 2 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

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…

cs.AI2026

Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron

Sahong Park, Suhwan Park, Hoyoung Lee +8

Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific investment preferences. We study…

cs.AI2026

When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

Hoyoung Lee, Suhwan Park, Seunghan Lee +15

Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…

cs.CL2026

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…

cs.LG2026★ 1 cited

Evaluating LLMs in Finance Requires Explicit Bias Consideration

Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7

Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…