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Found 12 papers, 4 papers with code
LongReward: Improving Long-context Large Language Models with AI Feedback
Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuning (SFT) often affects the long-context performance of SFT models and leads to inherent limitations.
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA
Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations.
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases
Secondly, KB-Plugin utilizes abundant annotated data from a rich-resourced KB to train another pluggable module, namely PI plugin, which can help the LLM extract question-relevant schema information from the schema plugin of any KB and utilize this information to induce programs over this KB.
Education distillation:getting student models to learn in shcools
Specifically, it is proposed to take fragmented student models divided from the complete student model as lower-grade models.
Self-Organization Towards $1/f$ Noise in Deep Neural Networks
In this study, we find that such $1/f$ noise is also found in deep neural networks trained on natural language, resembling that of their biological counterparts.
FedSSC: Shared Supervised-Contrastive Federated Learning
We propose Supervised Contrastive Federated Learning in which devices can share the learned class-wise feature spaces with each other and add the supervised-contrastive learning loss as a regularization term to foster the feature space learning.
Interactive Contrastive Learning for Self-supervised Entity Alignment
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments.
Edge of chaos as a guiding principle for modern neural network training
Motivated by the edge of chaos principle behind the optimal performance of neural networks, we study the role of various hyperparameters in modern neural network training algorithms in terms of the order-chaos phase diagram.
Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media.
Collaborative Inference for Efficient Remote Monitoring
While current machine learning models have impressive performance over a wide range of applications, their large size and complexity render them unsuitable for tasks such as remote monitoring on edge devices with limited storage and computational power.
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
Despite detection of suicidal ideation on social media has made great progress in recent years, people's implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance.
Optimal Machine Intelligence at the Edge of Chaos
It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos.
