https://github.com/Alidar40/lez-rus-azj-translator
공식 코드Neural machine translation system for Lezgian, Russian and Azerbaijani languages
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Neural machine translation system for Lezgian, Russian and Azerbaijani languages
Energy-Based Reward Models for Robust Language Model Alignment
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models
DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining
Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach
AdaFisher: Adaptive Second Order Optimization via Fisher Information
Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base
AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
A Bayesian Multilingual Document Model for Zero-shot Topic Identification and Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.