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Found 17 papers, 3 papers with code
QuantBench: Benchmarking AI Methods for Quantitative Investment
The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices.
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance.
Financial Wind Tunnel: A Retrieval-Augmented Market Simulator
To address this challenge, we propose Financial Wind Tunnel (FWT), a retrieval-augmented market simulator designed to generate controllable, reasonable, and adaptable market dynamics for model testing.
MasRouter: Learning to Route LLMs for Multi-Agent Systems
Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face challenges in dynamic LLM selection.
Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making
To overcome these challenges, we propose Guided Learning, a novel methodological framework designed to enhance end-to-end learning in multi-stage decision-making.
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Firstly, the Retrieval module gathers supporting triples from the KG, collects plausible candidate answers from a base embedding model, and retrieves context for each related entity.
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance.
ChartMoE: Mixture of Expert Connector for Advanced Chart Understanding
Combined with the vanilla connector, we initialize different experts in four distinct ways and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters.
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities.
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry.
Financial Knowledge Large Language Model
Secondly, we propose IDEA-FinKER, a Financial Knowledge Enhancement framework designed to facilitate the rapid adaptation of general LLMs to the financial domain, introducing a retrieval-based few-shot learning method for real-time context-level knowledge injection, and a set of high-quality financial knowledge instructions for fine-tuning any general LLM.
Context Graph
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples.
ChartBench: A Benchmark for Complex Visual Reasoning in Charts
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in image understanding and generation.
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries.
Fast Gumbel-Max Sketch and its Applications
The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval.
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems
The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i. e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction.
Fast Generating A Large Number of Gumbel-Max Variables
Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.
