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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/tomerwul/MegaSpeech
공식 코드Towards Hate Speech Detection at Large via Deep Generative Modeling
https://github.com/tomlimi/entangled_in_scripts
공식 코드https://github.com/tommasoc80/HateBERT
공식 코드HateBERT: Retraining BERT for Abusive Language Detection in English
https://github.com/tomsherborne/tram_optimizer
공식 코드TRAM: Bridging Trust Regions and Sharpness Aware Minimization
https://github.com/toneli/petailor-for-bio-triple-extraction
공식 코드Benchingmaking Large Langage Models in Biomedical Triple Extraction
https://github.com/toneli/rt-retrieving-and-thinking
공식 코드How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain
https://github.com/tongjifinlab/cfbenchmark
공식 코드CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
https://github.com/tongjifinlab/cfgpt
공식 코드CFGPT: Chinese Financial Assistant with Large Language Model
https://github.com/tongzhou21/CogMG
공식 코드CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph
https://github.com/tongzhou21/oasis
공식 코드Oasis: Data Curation and Assessment System for Pretraining of Large Language Models
https://github.com/tonichopp/ecamp
공식 코드ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
https://github.com/tonychenxyz/selfie
공식 코드SelfIE: Self-Interpretation of Large Language Model Embeddings
https://github.com/tonylekhtman/dilbert
공식 코드https://github.com/tonytu16/protigeno
공식 코드ProtiGeno: a prokaryotic short gene finder using protein language models
https://github.com/tonyzhang617/nomad-dist
공식 코드NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention
https://github.com/tool-lmm/tool-lmm
공식 코드MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
https://github.com/towhidabsar/communityneeds
공식 코드Community Needs and Assets: A Computational Analysis of Community Conversations
https://github.com/tpekarekrosin/uhh_ub_ageawarehri
공식 코드A Framework for Adapting Human-Robot Interaction to Diverse User Groups
https://github.com/traffic-alpha/illm-tsc
공식 코드https://github.com/traffic-alpha/llm-assisted-light
공식 코드https://github.com/trapoom555/language-model-sts-cft
공식 코드Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning
https://github.com/travis-xu/TR-Feedback
공식 코드Enhancing Tool Retrieval with Iterative Feedback from Large Language Models
https://github.com/treeai-lab/awesome-kv-cache-management
공식 코드A Survey on Large Language Model Acceleration based on KV Cache Management
https://github.com/treeai-lab/numericbench
공식 코드https://github.com/trestad/cogattn
공식 코드https://github.com/trestad/mitigating-reversal-curse
공식 코드https://github.com/trishullab/LibraryAugmentedSymbolicRegression.jl
공식 코드https://github.com/trishullab/copra
공식 코드https://github.com/trokhymovych/ki_multilingual_training
공식 코드https://github.com/trust-nlp/temporalassessment
공식 코드Time Matters: Examine Temporal Effects on Biomedical Language Models
https://github.com/trustedllm/llmdet
공식 코드LLMDet: A Third Party Large Language Models Generated Text Detection Tool
https://github.com/trustedllm/regavae
공식 코드RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
https://github.com/trustworthy-ml-lab/cb-llms
공식 코드https://github.com/trustworthy-software/dexbert
공식 코드DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
https://github.com/tryptofanik/gc-bert
공식 코드Enriching language models with graph-based context information to better understand textual data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.