Search Results for author:
Found 9 papers, 7 papers with code
SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model
Recent advances in deep learning have revolutionized seismic monitoring, yet developing a foundation model that performs well across multiple complex tasks remains challenging, particularly when dealing with degraded signals or data scarcity.
BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
By leveraging weight decomposition, BitStack can dynamically adjust the model size with minimal transmission between running memory and storage devices.
Concise and Precise Context Compression for Tool-Using Language Models
However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
MetaPrompting: Learning to Learn Better Prompts
Prompting method is regarded as one of the crucial progress for few-shot nature language processing.
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
A Self-supervised Method for Entity Alignment
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.
