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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/chiangel/awesome-llm-for-recsys
공식 코드How Can Recommender Systems Benefit from Large Language Models: A Survey
https://github.com/chihyaoma/cyclical-visual-captioning
공식 코드Learning to Generate Grounded Visual Captions without Localization Supervision
https://github.com/chijames/kerple
공식 코드KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
https://github.com/chilab1/unipagpt-23
공식 코드Unipa-GPT: Large Language Models for university-oriented QA in Italian
https://github.com/chillingdream/dap
공식 코드Dual-Alignment Pre-training for Cross-lingual Sentence Embedding
https://github.com/chingheng0808/PromptHSI
공식 코드https://github.com/chocowu/lasuie
공식 코드https://github.com/chongqingnosubway/prompt-ot
공식 코드https://github.com/choosewhatulike/cluster-clip
공식 코드Balanced Data Sampling for Language Model Training with Clustering
https://github.com/chrischenhub/OnlySportsLM
공식 코드https://github.com/chrisdonahue/ilm
공식 코드https://github.com/chrisliu298/llm-unlearn-eco
공식 코드Large Language Model Unlearning via Embedding-Corrupted Prompts
https://github.com/chrispapa2000/maskgst
공식 코드Masked Generative Story Transformer with Character Guidance and Caption Augmentation
https://github.com/christianvedels/occcanine
공식 코드Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINE
https://github.com/christos42/CLDR_CLNER_models
공식 코드Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
https://github.com/chuangtaochen-tum/livemind
공식 코드LiveMind: Low-latency Large Language Models with Simultaneous Inference
https://github.com/chuanmeng/qpp-genre
공식 코드Query Performance Prediction using Relevance Judgments Generated by Large Language Models
https://github.com/chuanmeng/rlt4reranking
공식 코드Ranked List Truncation for Large Language Model-based Re-Ranking
https://github.com/chuhac/atm-rag
공식 코드ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
https://github.com/chuxiaokai/baidu_ultr_dataset
공식 코드https://github.com/chuzhumin98/convsearch-dataset
공식 코드Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
https://github.com/cibr-okubo-lab/speechbci_2024
공식 코드https://github.com/cicl-stanford/moral-evals
공식 코드Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models
https://github.com/cifkao/context-probing
공식 코드Black-box language model explanation by context length probing
https://github.com/cindyxinyiwang/deep-latent-sequence-model
공식 코드A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/ciol-researchlab/ukil
공식 코드Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling
https://github.com/circle-hit/moei
공식 코드https://github.com/circleradon/osprey
공식 코드https://github.com/circleradon/tokenpacker
공식 코드https://github.com/cisco-open/DeepVision/tree/main/recallm
공식 코드RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models
https://github.com/cisnlp/GlotScript
공식 코드GlotScript: A Resource and Tool for Low Resource Writing System Identification
https://github.com/cisnlp/glot500
공식 코드Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
https://github.com/cisnlp/langsamp
공식 코드https://github.com/cisnlp/ofa
공식 코드https://github.com/cisnlp/xampler
공식 코드XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.