Search Results for author:
Found 12 papers, 3 papers with code
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications
We first define three tasks based on the unique characteristics of the financial domain: 1) ASR for short financial audio, 2) ASR for long financial audio, and 3) summarization of long financial audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks.
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent
Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance.
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs.
RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data
The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making.
TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance
In financial trading contexts, LLMs serve as the decision core for trading agents, leveraging their layered memory system to integrate multi-source historical actions and market insights.
Actively learning a Bayesian matrix fusion model with deep side information
High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli.
Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise
Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency.
