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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/ChengyueGongR/FrequencyAgnostic
공식 코드https://github.com/ChengyueGongR/advsoft
공식 코드https://github.com/ChenxiLiu-HNU/ST-LLM
공식 코드Spatial-Temporal Large Language Model for Traffic Prediction
https://github.com/ChunyuanLI/Optimus
공식 코드Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
https://github.com/ClaudiuCreanga/semeval-2024-task-8
공식 코드Transformer based neural networks for emotion recognition in conversations
https://github.com/CogComp/iclr_database
공식 코드Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach
https://github.com/Cold-Winter/vqs
공식 코드https://github.com/Colin97/PartSLIP_page
공식 코드PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
https://github.com/Comcast/llm-stability
공식 코드https://github.com/CompNet/conivel
공식 코드Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
https://github.com/Cranial-XIX/longhorn
공식 코드https://github.com/CryhanFang/CLIP2Video
공식 코드https://github.com/CyberAgentAILab/annotation-efficient-po
공식 코드Annotation-Efficient Preference Optimization for Language Model Alignment
https://github.com/CyberAgentAILab/diverse-mbr
공식 코드Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
https://github.com/CyberAgentAILab/regularized-bon
공식 코드Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment
https://github.com/DAI-Lab/fibber
공식 코드https://github.com/DFKI-NLP/DISTRE
공식 코드Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
https://github.com/DFKI-NLP/OLM
공식 코드Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
https://github.com/DI4IR/SIGIR2021
공식 코드https://github.com/DM1122/LLMSat
공식 코드LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration
https://github.com/DRAGNLabs/towards-neural-programming-interfaces
공식 코드https://github.com/DRSY/KV_Compression
공식 코드Context Compression for Auto-regressive Transformers with Sentinel Tokens
https://github.com/DSE-MSU/R-transformer
공식 코드R-Transformer: Recurrent Neural Network Enhanced Transformer
https://github.com/DSIP-FBK/GPTCast
공식 코드GPTCast: a weather language model for precipitation nowcasting
https://github.com/DSPsleeporg/smiles-transformer
공식 코드SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
https://github.com/Dan123dan/rGBN-RNN
공식 코드Recurrent Hierarchical Topic-Guided RNN for Language Generation
https://github.com/Daniel-Gong/ChatGPT_WM
공식 코드https://github.com/DanielLin94144/Full-Duplex-Bench
공식 코드https://github.com/DanielSc4/RewardLM
공식 코드https://github.com/DannyMerkx/caption2image
공식 코드Learning semantic sentence representations from visually grounded language without lexical knowledge
https://github.com/DannyMerkx/next_word_prediction
공식 코드https://github.com/DannyMerkx/speech2image
공식 코드https://github.com/DarkPr0digy/MORPH_SEGMENT
공식 코드Canonical and Surface Morphological Segmentation for Nguni Languages
https://github.com/Darwinkel/bachelor-thesis-information-science
공식 코드Fingerprinting web servers through Transformer-encoded HTTP response headers
https://github.com/Deagogishvili/chapter-multi-task
공식 코드PatchProt: Hydrophobic patch prediction using protein foundation models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.