Challenge: Traditional methods of alpha mining have inherent limitations, especially in implementing the ideas of quant researchers.
Approach: They propose a new alpha mining paradigm by introducing human-AI interaction and a prompt engineering algorithmic framework to implement this paradigm by using large language models.
Outcome: The proposed framework is based on human-AI interaction and large language models and is comparable to human participants in the WorldQuant International Quant Championship.

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Challenge: Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio.
Approach: They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Outcome: The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

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Challenge: Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
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Outcome: The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.
A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics (2025.emnlp-industry)

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Challenge: Recent advances in Large Language Models (LLMs) have opened up promising new avenues by enhancing reasoning and inference capabilities across diverse data and information sources.
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Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)

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Challenge: Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors.
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Evaluating AI for Finance: Is AI Credible at Assessing Investment Risk Appetite? (2025.emnlp-industry)

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Challenge: Our analysis was conducted on proprietary systems and open-weight models . FINRISKEVAL analyzed 1,720 profiles spanning a broad spectrum of possible risk categories .
Approach: They evaluated proprietary AI systems and open-weight models to assess investment risk appetite using carefully curated user profiles.
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Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)

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Challenge: Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding .
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AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)

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Challenge: Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias.
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GPTScore: Evaluate as You Desire (2024.naacl-long)

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Challenge: Existing evaluation frameworks for text generation are not adequate to assess the quality of the generated outputs.
Approach: They propose a framework that utilizes emergent abilities of generative pre-trained models to evaluate generated texts.
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AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models (2022.findings-emnlp)

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Challenge: Existing approaches to improve inference efficiency by accelerating model fine-tuning have not been thoroughly explored.
Approach: They propose to combine parameter-efficient adaptation and model compression to accelerate model . they propose to freeze binary parameters and scale scaling factors for target tasks .
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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)

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Challenge: Recent advances in generative language models have enabled machines to generate realistic texts.
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