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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/tensorgi/t6
공식 코드https://github.com/terry-cyx/metaopenfoam
공식 코드https://github.com/terry-r123/RNABenchmark
공식 코드BEACON: Benchmark for Comprehensive RNA Tasks and Language Models
https://github.com/terrypei/efficientvmamba
공식 코드EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
https://github.com/teshnizi/optimus
공식 코드OptiMUS: Optimization Modeling Using MIP Solvers and large language models
https://github.com/teslacool/SCA
공식 코드Soft Contextual Data Augmentation for Neural Machine Translation
https://github.com/text-machine-lab/extending_psycholinguistic_dataset
공식 코드Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning
https://github.com/text-machine-lab/mini_bert
공식 코드Honey, I Shrunk the Language: Language Model Behavior at Reduced Scale
https://github.com/text2app/Text2App
공식 코드Text2App: A Framework for Creating Android Apps from Text Descriptions
https://github.com/texttron/hyde
공식 코드https://github.com/th-nuernberg/crisis-tapt-hmc
공식 코드https://github.com/thanhlecongg/Invalidator
공식 코드Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning
https://github.com/thanhnx12/cre-via-mmi
공식 코드Preserving Generalization of Language models in Few-shot Continual Relation Extraction
https://github.com/the-deep-nlp/bias-aware-humanitarian-entry-classification
공식 코드Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification
https://github.com/the-martyr/causalmm
공식 코드https://github.com/theNamek/Bias-Neurons
공식 코드https://github.com/theia-4869/fastervlm
공식 코드Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs
https://github.com/theia-4869/vispruner
공식 코드Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs
https://github.com/thennal10/fisher-nodes-merging
공식 코드https://github.com/theodorblackbird/lina-speech
공식 코드Small-E: Small Language Model with Linear Attention for Efficient Speech Synthesis
https://github.com/thepurpleowl/codequeries-benchmark
공식 코드https://github.com/theryanl/egs
공식 코드Improving Interpersonal Communication by Simulating Audiences with Language Models
https://github.com/theworldofagents/agentic-reasoning
공식 코드Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
https://github.com/thibaultdouzon/business-document-pre-training
공식 코드Improving Information Extraction on Business Documents with Specific Pre-Training Tasks
https://github.com/thijmennijdam/NevIR-reproducibility
공식 코드https://github.com/thiliniiw/llms-synt-struct-sentence-analogies
공식 코드https://github.com/thinkwee/iAgents
공식 코드Autonomous Agents for Collaborative Task under Information Asymmetry
https://github.com/thomlake/investigating-alignment
공식 코드From Distributional to Overton Pluralism: Investigating Large Language Model Alignment
https://github.com/thompsonb/prism
공식 코드https://github.com/thongnt99/lsr-long
공식 코드https://github.com/threestudio-project/threestudio
공식 코드Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
https://github.com/thu-bpm/chatgpt-sql
공식 코드A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability
https://github.com/thu-bpm/watermark-radioactivity-attack
공식 코드Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?
https://github.com/thu-coai/HINT
공식 코드Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
https://github.com/thu-coai/SentiLARE
공식 코드SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.