Language Modelling
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/strong-ai-lab/logical-equivalence-driven-amr-data-augmentation-for-representation-learning
공식 코드Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
https://github.com/strong-ai-lab/modular-lm
공식 코드Can Large Language Models Learn Independent Causal Mechanisms?
https://github.com/studio-ousia/luke
공식 코드https://github.com/studyzx/gesturegpt_iss
공식 코드GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents
https://github.com/stupidbuluchacha/urbanclip
공식 코드https://github.com/styfeng/GenAug
공식 코드https://github.com/styfeng/tinydialogues
공식 코드Is Child-Directed Speech Effective Training Data for Language Models?
https://github.com/subbareddy248/speech-llm-brain
공식 코드Speech language models lack important brain-relevant semantics
https://github.com/sufe-aiflm-lab/fin-r1
공식 코드Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
https://github.com/sufenlp/accalign
공식 코드Multilingual Sentence Transformer as A Multilingual Word Aligner
https://github.com/sufianj/def_extraction
공식 코드Extracting Definienda in Mathematical Scholarly Articles with Transformers
https://github.com/sugarandgugu/GaVaMoE
공식 코드GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
https://github.com/sujeongim/labtop
공식 코드LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records
https://github.com/sulcantonin/llm_neuralips23
공식 코드PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators
https://github.com/sumin124/mv-clam
공식 코드MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
https://github.com/sumit-research/lmcore
공식 코드LM-CORE: Language Models with Contextually Relevant External Knowledge
https://github.com/sumitnitkkr/PSAT
공식 코드https://github.com/sunanhe/mkt
공식 코드Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
https://github.com/sungraepark/AdvDrop-sMNIST
공식 코드https://github.com/sungraepark/adversarial_dropout_text_classification
공식 코드https://github.com/sunisfighting/signa
공식 코드Single-View Graph Contrastive Learning with Soft Neighborhood Awareness
https://github.com/sunnweiwei/co-bench
공식 코드CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
https://github.com/sunset-clouds/DAVAM
공식 코드Discrete Auto-regressive Variational Attention Models for Text Modeling
https://github.com/sunsmarterjie/chatterbox
공식 코드https://github.com/sunxiaojie99/attempt
공식 코드Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
https://github.com/sunxiaojie99/mural
공식 코드A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
https://github.com/sunyilgdx/prompts4keras
공식 코드https://github.com/sunzey/x-prompt
공식 코드https://github.com/superMereo/generating-event-descriptions
공식 코드Generating event descriptions under syntactic and semantic constraints
https://github.com/superagi/veagle
공식 코드https://github.com/superjamessyx/generative-foundation-ai-assistant-for-pathology
공식 코드PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
https://github.com/suprityoung/zhongjing
공식 코드https://github.com/sustcsonglin/TN-PCFG
공식 코드Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs
https://github.com/sustcsonglin/flash-linear-attention
공식 코드Linear Transformers with Learnable Kernel Functions are Better In-Context Models
https://github.com/suu990901/Dial-MAE
공식 코드Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.