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Found 20 papers, 7 papers with code
UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection
UFO-RL leverages this metric to select data within the estimated ZPD for training.
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection
Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning.
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection
In this work, we propose an Exemplar-Guided Reflection with Memory mechanism (ERM) to realize more efficient and accurate prompt optimization.
Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
We find that: (1) LLMs selectively activate task-specific attention heads during SFT; (2) activation patterns for complex tasks are combinations of basic task patterns; and (3) changes in a few parameters can significantly impact activation patterns after SFT on a small number of samples. Based on these insights, experiments are conducted to actually enhance the efficiency and effectiveness of SFT.
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
This makes it a challenge to diagnose and remedy the deficiencies of LLMs through rich label-free user queries.
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
The results show that our approach not only boosts the general reasoning performance of LLMs but also makes considerable strides towards their capacity for abstract reasoning, moving beyond simple memorization or imitation to a more nuanced understanding and application of generic facts.
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.
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.
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.
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.
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.
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.
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.
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.
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.
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
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.
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.
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.
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.
