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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/laihuiyuan/mmfld
공식 코드https://github.com/lairikeqia/zc3
공식 코드https://github.com/laiyuzhi/nvp-hri
공식 코드NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language Model
https://github.com/lakeraai/dsec-gandalf
공식 코드https://github.com/lalalamdbf/plse_idrr
공식 코드Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
https://github.com/lamda-bbo/universal-offline-bbo
공식 코드Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
https://github.com/lamda-rl/imaginebench
공식 코드ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
https://github.com/lamm-mit/PRefLexOR
공식 코드https://github.com/lamm-mit/graph-aware-transformers
공식 코드Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers
https://github.com/lamm-mit/materiomictransformer
공식 코드https://github.com/lamm-mit/mechagents
공식 코드https://github.com/lamm-mit/protagents
공식 코드https://github.com/lamp-benchmark/lamp
공식 코드Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation
https://github.com/lamvin/PeerReviewAI
공식 코드https://github.com/lancopku/DPGAN
공식 코드https://github.com/lancopku/Explicit-Sparse-Transformer
공식 코드Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
https://github.com/lancopku/codable-watermarking-for-llm
공식 코드Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
https://github.com/lang-uk/dragoman
공식 코드Setting up the Data Printer with Improved English to Ukrainian Machine Translation
https://github.com/langprocgroup/rnn_soft_constraints
공식 코드https://github.com/langus0/critic-aware-decoding
공식 코드Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation
https://github.com/lanwuwei/GigaBERT
공식 코드An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
https://github.com/lanwuwei/SPM_toolkit
공식 코드https://github.com/lanxiang1017/language-modeling-on-tabular-data-survey
공식 코드Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution
https://github.com/lanzer-tree/ras-eval
공식 코드https://github.com/laohur/gur
공식 코드https://github.com/laquabe/OneNet
공식 코드OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting
https://github.com/lara-martin/StoryPlot-RewardShaping
공식 코드Controllable Neural Story Plot Generation via Reward Shaping
https://github.com/larspars/word-rnn
공식 코드Shifting Mean Activation Towards Zero with Bipolar Activation Functions
https://github.com/lartpang/ovcamo
공식 코드https://github.com/lasigeBioTM/ChemRecSys
공식 코드https://github.com/lasmp23/lasmp
공식 코드https://github.com/laujames/pat
공식 코드Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
https://github.com/launchnlp/PROF
공식 코드https://github.com/launchnlp/actcab
공식 코드Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decoding
https://github.com/launchnlp/litcab
공식 코드LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.