Language Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 55
- 보존된 평가 기록
- 466
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/abderrahmanskiredj/DarijaBanking
공식 코드https://github.com/abdoelsayed2016/grg
공식 코드Generator-Retriever-Generator Approach for Open-Domain Question Answering
https://github.com/abdur75648/v-zen
공식 코드V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM
https://github.com/abecode/emo20q
공식 코드BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions
https://github.com/abel2code/the_butterfly_effect_of_prompts
공식 코드https://github.com/abhijitmishra/sentinellm-aaai2024
공식 코드https://github.com/abhinand5/tamil-llama
공식 코드https://github.com/abhishekaich27/GAMA-pytorch
공식 코드https://github.com/abhishekpanigrahi1996/transformer_in_transformer
공식 코드https://github.com/ablustrund/loramoe
공식 코드LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
https://github.com/abman23/on-device-ai-comm
공식 코드Integrating Pre-Trained Language Model with Physical Layer Communications
https://github.com/abman23/on-device-llm-wifi-roaming
공식 코드https://github.com/aboveparadise/llmcbench
공식 코드LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
https://github.com/abrazinskas/FewSum
공식 코드https://github.com/abteen/americasnli
공식 코드https://github.com/ace117mc/transformer-models-covid
공식 코드https://github.com/acfr/cassdetect
공식 코드https://github.com/achandlr/DEEP
공식 코드https://github.com/acharkq/molca
공식 코드MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
https://github.com/acharkq/next-mol
공식 코드NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
https://github.com/acmi-lab/mvp
공식 코드Model-tuning Via Prompts Makes NLP Models Adversarially Robust
https://github.com/acolas1/GAP_COLING2022
공식 코드GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation
https://github.com/adamits/morphological-inflection-noise
공식 코드https://github.com/adamkarvonen/SAE_BoardGameEval
공식 코드Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
https://github.com/adap/flower
공식 코드https://github.com/adapter-hub/xgqa
공식 코드https://github.com/adbrei/renargen
공식 코드Returning to the Start: Generating Narratives with Related Endpoints
https://github.com/adeeph/memeclassification
공식 코드https://github.com/adeline-cs/GTR
공식 코드Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
https://github.com/adresgezgini/NLP4AT
공식 코드Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods
https://github.com/adrianbzg/hyperbert
공식 코드https://github.com/adrianbzg/sfavel
공식 코드Unsupervised Pretraining for Fact Verification by Language Model Distillation
https://github.com/adrianbzg/tinyroberta-distillation-qa-es
공식 코드Language Model Knowledge Distillation for Efficient Question Answering in Spanish
https://github.com/adriangonz/statistical-nlp-17
공식 코드https://github.com/ads-ai/k12-bert-aied-2022
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.