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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/zihangdai/mos
공식 코드Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/zihanwangki/fffner
공식 코드https://github.com/zihanwangki/goalex
공식 코드Goal-Driven Explainable Clustering via Language Descriptions
https://github.com/zihaod/musilingo
공식 코드https://github.com/zihaow123/unimm
공식 코드Unified Multimodal Model with Unlikelihood Training for Visual Dialog
https://github.com/zijian678/tdd
공식 코드Unveiling and Manipulating Prompt Influence in Large Language Models
https://github.com/ziliwang/MSnet-for-Gendered-Pronoun-Resolution
공식 코드https://github.com/zimingyy/subzero
공식 코드https://github.com/zinengtang/VidLanKD
공식 코드VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
https://github.com/zinengtang/perceiver_vl
공식 코드Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
https://github.com/zipengxuc/ppe
공식 코드https://github.com/zirui-ray-liu/wtacrs
공식 코드Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
https://github.com/zishan-ai/neg
공식 코드INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System
https://github.com/zitongyang/synthetic_continued_pretraining
공식 코드https://github.com/zivaharoni/gradual-learning-rnn
공식 코드https://github.com/zixuanke/pycontinual
공식 코드Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zjlab-ammi/llm4teach
공식 코드Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
https://github.com/zjr2000/llmva-gebc
공식 코드LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
https://github.com/zju-vipa/odyssey
공식 코드https://github.com/zjukeliu/s2snet
공식 코드S2SNet: A Pretrained Neural Network for Superconductivity Discovery
https://github.com/zjukg/conferenceqa
공식 코드Reliable Academic Conference Question Answering: A Study Based on Large Language Model
https://github.com/zjukg/kg-mm-survey
공식 코드Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/kopa
공식 코드Making Large Language Models Perform Better in Knowledge Graph Completion
https://github.com/zjumai/evopatient
공식 코드LLMs Can Simulate Standardized Patients via Agent Coevolution
https://github.com/zjunlp/BiasEdit
공식 코드BiasEdit: Debiasing Stereotyped Language Models via Model Editing
https://github.com/zjunlp/DART
공식 코드Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
https://github.com/zjunlp/Deco
공식 코드MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
https://github.com/zjunlp/DeepKE/tree/main/example/llm
공식 코드CodeKGC: Code Language Model for Generative Knowledge Graph Construction
https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot
공식 코드LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
https://github.com/zjunlp/Kformer
공식 코드Kformer: Knowledge Injection in Transformer Feed-Forward Layers
https://github.com/zjunlp/KnowPrompt
공식 코드KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
https://github.com/zjunlp/MolGen
공식 코드https://github.com/zjunlp/Prompt4ReasoningPapers
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.