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Found 15 papers, 6 papers with code
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning
ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.
PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments
We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios.
One-Shot General Object Localization
In contrast, our proposed OneLoc algorithm efficiently finds the object center and bounding box size by a special voting scheme.
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus despite noticeable inter-inconsistencies, but imbalances in their abilities can lead to domination by superior LLMs.
Quda: Natural Language Queries for Visual Data Analytics
To address this challenge, we present a new dataset, called Quda, that aims to help V-NLIs recognize analytic tasks from free-form natural language by training and evaluating cutting-edge multi-label classification models.
Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method
Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
A Graph Enhanced BERT Model for Event Prediction
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
Improving Cross-Task Generalization with Step-by-Step Instructions
Instruction tuning has been shown to be able to improve cross-task generalization of language models.
Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts
Drawing on cognitive theory, we target the first scenario of decision-making styles where there is no superiority in the conflict and categorize LLMs' preference into dependent, intuitive, and rational/irrational styles.
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance.
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility
Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems.
Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence.
