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Found 9 papers, 4 papers with code
Position: Standard Benchmarks Fail -- LLM Agents Present Overlooked Risks for Financial Applications
Current financial LLM agent benchmarks are inadequate.
Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making
In many existing human + AI systems, decision-making support is typically provided in the form of text explanations (XAI) to help users understand the AI's reasoning.
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning.
GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets
To address this gap, we introduce GraphEval2000, the first comprehensive graph dataset, comprising 40 graph data structure problems along with 2000 test cases.
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge.
LMExplainer: Grounding Knowledge and Explaining Language Models
By comparing the explanations generated by LMExplainer with those of other models, we show that our approach offers more comprehensive and clearer explanations of the reasoning process.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
Towards Toxic and Narcotic Medication Detection with Rotated Object Detector
Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry.
