https://github.com/google-research/google-research/tree/master/t5_closed_book_qa
공식 코드How Much Knowledge Can You Pack Into the Parameters of a Language Model?
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
How Much Knowledge Can You Pack Into the Parameters of a Language Model?
Thieves on Sesame Street! Model Extraction of BERT-based APIs
LongT5: Efficient Text-To-Text Transformer for Long Sequences
Predicting from Strings: Language Model Embeddings for Bayesian Optimization
Investigating Efficiently Extending Transformers for Long Input Summarization
General-purpose, long-context autoregressive modeling with Perceiver AR
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Discrete Flows: Invertible Generative Models of Discrete Data
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Hierarchical Transformers Are More Efficient Language Models
Matryoshka Query Transformer for Large Vision-Language Models
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation
Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives
Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model
The Quantified Boolean Bayesian Network: Theory and Experiments with a Logical Graphical Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.