https://github.com/tatsu-lab/stanford_alpaca
공식 코드Self-Instruct: Aligning Language Models with Self-Generated Instructions
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Self-Instruct: Aligning Language Models with Self-Generated Instructions
SCROLLS: Standardized CompaRison Over Long Language Sequences
Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
Light Coreference Resolution for Russian with Hierarchical Discourse Features
Bilinear MLPs enable weight-based mechanistic interpretability
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
Chaining thoughts and LLMs to learn DNA structural biophysics
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation
On the Expressivity Role of LayerNorm in Transformers' Attention
Learning Deterministic Weighted Automata with Queries and Counterexamples
Debiasing Methods in Natural Language Understanding Make Bias More Accessible
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation
On the Multilingual Capabilities of Very Large-Scale English Language Models
Accelerating Vision-Language Pretraining with Free Language Modeling
SEED-Story: Multimodal Long Story Generation with Large Language Model
Scaling Capability in Token Space: An Analysis of Large Vision Language Model
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.