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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/zjunlp/PromptKG/tree/main/research/GenKGC
공식 코드From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
https://github.com/zjunlp/SemEval2021Task4
공식 코드https://github.com/zjunlp/deepke
공식 코드CodeKGC: Code Language Model for Generative Knowledge Graph Construction
https://github.com/zjunlp/easyedit
공식 코드EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
https://github.com/zjunlp/knowledge-rumination
공식 코드https://github.com/zjunlp/knowledgecircuits
공식 코드https://github.com/zjunlp/knowlm
공식 코드https://github.com/zjunlp/promptkg
공식 코드LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/zjunlp/steer-target-atoms
공식 코드Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
https://github.com/zjunlp/trice
공식 코드Making Language Models Better Tool Learners with Execution Feedback
https://github.com/zjwang21/syncs
공식 코드Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
https://github.com/zl-liu/radiation-oncology-nlp-database
공식 코드https://github.com/zlinao/VGLM
공식 코드Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
https://github.com/zmzhang2000/MIGCN
공식 코드Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
https://github.com/znlp/x-instruction
공식 코드https://github.com/zodiark-ch/emergence-of-llms
공식 코드https://github.com/zodiark-ch/language-skill-of-llms
공식 코드https://github.com/zodiark-ch/masters-of-our-emnlp2023-papers
공식 코드Learning a Structural Causal Model for Intuition Reasoning in Conversation
https://github.com/zomss/co-prompt
공식 코드Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
https://github.com/zonetrooper32/robot_semantics
공식 코드https://github.com/zorazrw/agent-workflow-memory
공식 코드https://github.com/zou-group/avatar
공식 코드AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
https://github.com/zouharvi/sentence-embd-fusion
공식 코드Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Models
https://github.com/zr2021/2021_qq_aiac_tack1_1st
공식 코드Top1 Solution of QQ Browser 2021 Ai Algorithm Competition Track 1 : Multimodal Video Similarity
https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/zrrskywalker/mavis
공식 코드MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
https://github.com/zsquaredz/adapt_vs_finetune
공식 코드To Adapt or to Fine-tune: A Case Study on Abstractive Summarization
https://github.com/zsquaredz/subpopulation_analysis
공식 코드Understanding Domain Learning in Language Models Through Subpopulation Analysis
https://github.com/zuela-ai/angofa
공식 코드ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language Model
https://github.com/zukang-liao/context-sensitive-image-similarity
공식 코드Image Similarity using An Ensemble of Context-Sensitive Models
https://github.com/zurichnlp/recognizing-semantic-differences
공식 코드Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
https://github.com/zurichnlp/swissbert
공식 코드https://github.com/zwanah/DataVisT5
공식 코드DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization
https://github.com/zwenyu/colearn-plus
공식 코드Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training
https://github.com/zwq2018/multi-modal-self-instruct
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.