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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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5 results for “GPT-4-Turbo”

FinanceBench: A New Benchmark for Financial Question Answeringcs.CL

FinanceBench benchmarks LLMs on financial QA, revealing limitations.

problem Evaluating LLMs' performance on financial question answering.

method Developed a comprehensive test suite (FinanceBench) with 10,231 questions, tested 16 models, and manually reviewed answers.

result Existing LLMs have significant limitations for financial QA, especially GPT-4-Turbo.

financial QA LLMs benchmarking GPT-4-Turbo hallucinations

2023-11-20abs ↗pdf ↗

AI Trading: Evaluating Large Language Models for Technical Market Analysiscs.LG

This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.

problem Evaluating LLMs for technical market analysis in financial markets.

method Structured evaluation of five LLMs (GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, FinGPT) on four tasks: candlestick pattern recognition, directional signal generation, backtesting, and financial report comprehension.

result GPT-4 Turbo and FinGPT outperform passive benchmarks in simulated backtesting, with GPT-4 Turbo achieving the highest annualized return and Sharpe ratio.

Large Language Models Technical Market Analysis Financial Markets Backtesting Candlestick Patterns

2026-07-16abs ↗pdf ↗

Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?q-fin.RM

LLMs can help explain credit risk models but not autonomously.

problem Leveraging LLMs for post-hoc explainability in credit risk models.

method Comparison of LLM outputs with SHAP and coefficient-based attributions on three LMs.

result LLMs reliably preserve feature-importance rankings but poorly align with autonomous explanations.

Large Language Models Credit Risk Explainability Narrative Interfaces Autonomous Explanations

2026-02-21abs ↗pdf ↗

Self-Play Preference Optimization for Language Model Alignmentcs.LG

SPPO optimizes language model alignment by treating preferences as a game and achieving state-of-the-art performance.

problem Capturing intransitivity and irrationality in human preferences for accurate language model alignment.

method Self-play-based approach to identify Nash equilibrium policy through iterative policy updates.

result SPPO achieves state-of-the-art win-rate of 28.53% on AlpacaEval 2.0 without external supervision.

language model alignment reinforcement learning human preferences Nash equilibrium preference optimization

2024-05-01abs ↗pdf ↗

The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4cs.CY

This study analyzes financial equity research reports to identify frequently asked questions and automates 80% of them.

problem Insufficient empirical analysis of questions answered in financial equity research reports.

method Analyzed 72 financial equity research reports, classifying sentences into 169 unique question archetypes. Used public corporate reports to classify questions' potential for automation.

result Approximately 80% of financial equity research reports can be automated, with 78.7% of questions automatable.

financial equity research automated analysis large language models question classification information extraction

2024-07-04abs ↗pdf ↗