https://github.com/yegcjs/mixinglaws
공식 코드Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
Ranking-Enhanced Unsupervised Sentence Representation Learning
Spectral Editing of Activations for Large Language Model Alignment
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling
A Survey of Graph Meets Large Language Model: Progress and Future Directions
Few-Shot Object Detection via Association and DIscrimination
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model
What Has Been Enhanced in my Knowledge-Enhanced Language Model?
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents
Libra: Building Decoupled Vision System on Large Language Models
SharpZO: Hybrid Sharpness-Aware Vision Language Model Prompt Tuning via Forward-Only Passes
Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts
HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
Defending LLMs against Jailbreaking Attacks via Backtranslation
Defending LLMs against Jailbreaking Attacks via Backtranslation
FANformer: Improving Large Language Models Through Effective Periodicity Modeling
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Mixture of Attention Heads: Selecting Attention Heads Per Token
Pipette: Automatic Fine-grained Large Language Model Training Configurator for Real-World Clusters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.