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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/shannanyinxiang/viteraser
공식 코드https://github.com/shanzhenren/PLE
공식 코드Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
https://github.com/shaochenze/patchtrain
공식 코드Beyond Next Token Prediction: Patch-Level Training for Large Language Models
https://github.com/shaochenze/scoringruleslm
공식 코드https://github.com/shaojiejiang/ct-loss
공식 코드A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration
https://github.com/shaojintian/power_law_decay
공식 코드https://github.com/shaoxiongji/fed-att
공식 코드Learning Private Neural Language Modeling with Attentive Aggregation
https://github.com/sharif-slpl/se-2024-task-06-shroom
공식 코드SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
https://github.com/shark-nlp/cab
공식 코드CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
https://github.com/shark-nlp/evalm
공식 코드https://github.com/shark-nlp/openicl
공식 코드https://github.com/shark0-0/vg4d
공식 코드https://github.com/sharonlevy/contextualquestions
공식 코드https://github.com/shawnhuang497/bird
공식 코드A Refer-and-Ground Multimodal Large Language Model for Biomedicine
https://github.com/shawnhuang497/medplib
공식 코드Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
https://github.com/shawnricecake/agile-quant
공식 코드Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
https://github.com/shayankiyani98/cp
공식 코드https://github.com/sheikhshafayat/multi-fact
공식 코드Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
https://github.com/shengnanh20/lamp
공식 코드LAMP: Leveraging Language Prompts for Multi-person Pose Estimation
https://github.com/shenshuaijie/SDN
공식 코드SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
https://github.com/shentao-yang/preference_grounded_guidance
공식 코드Preference-grounded Token-level Guidance for Language Model Fine-tuning
https://github.com/shentianxiao/film
공식 코드https://github.com/shenxiaochenn/SongCi
공식 코드Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
https://github.com/shenzheng2000/tpsence
공식 코드TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain
https://github.com/sheryc/resonance_rope
공식 코드Resonance RoPE: Improving Context Length Generalization of Large Language Models
https://github.com/shihaozhaozsh/lavi-bridge
공식 코드Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
https://github.com/shijian2001/templatematters
공식 코드https://github.com/shijiawenwen/judgedeceiver
공식 코드Optimization-based Prompt Injection Attack to LLM-as-a-Judge
https://github.com/shijxcs/candle
공식 코드Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model
https://github.com/shimisalant/CWR
공식 코드Contextualized Word Representations for Reading Comprehension
https://github.com/shinying/dest
공식 코드https://github.com/shiranD/word_level_evaluation
공식 코드https://github.com/shirley-wu/text_to_table
공식 코드https://github.com/shivank21/Implicit_Preference_Optimization
공식 코드IPO: Your Language Model is Secretly a Preference Classifier
https://github.com/shiyaya/emscore
공식 코드EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.