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Found 9 papers, 1 papers with code
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.
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).
MultiPoT: Multilingual Program of Thoughts Harnesses Multiple Programming Languages
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
In this survey, we present these advances towards length extrapolation in a unified notation from the perspective of PE.
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models
Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications.
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
In recent years, pre-trained language models have undergone rapid development with the emergence of large-scale models.
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph.
Ranked #8 on
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on ogbl-wikikg2
Verification Code Recognition Based on Active and Deep Learning
However, the advantages of convolutional neural networks depend on the data used by the training classifier, particularly the size of the training set.
