https://github.com/alibaba-nlp/vec-ra-odqa
공식 코드Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
SAS: Self-Augmentation Strategy for Language Model Pre-training
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
Eliciting Latent Predictions from Transformers with the Tuned Lens
IFSeg: Image-free Semantic Segmentation via Vision-Language Model
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants
Neurocache: Efficient Vector Retrieval for Long-range Language Modeling
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Holodeck: Language Guided Generation of 3D Embodied AI Environments
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Continued Pretraining for Better Zero- and Few-Shot Promptability
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.