Language Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 55
- 보존된 평가 기록
- 466
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/LSquaredM/mutual_info_scaling_law
공식 코드L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling
https://github.com/LUMIA-Group/TosT5
공식 코드Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training
https://github.com/LUMII-Syslab/RRU
공식 코드https://github.com/LUMII-Syslab/RSE
공식 코드Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
https://github.com/LUMII-Syslab/shuffle-exchange
공식 코드Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time
https://github.com/LZhengisme/CODA
공식 코드https://github.com/LaraQianYang/Ouroboros
공식 코드Ouroboros: On Accelerating Training of Transformer-Based Language Models
https://github.com/LeapLabTHU/diver-ct
공식 코드DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints
https://github.com/LeonWlw/asr_blockformer
공식 코드Improving Mandarin Speech Recogntion with Block-augmented Transformer
https://github.com/Liang-Qiu/is13
공식 코드Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling Task
https://github.com/Lightning-AI/lit-gpt
공식 코드https://github.com/LinQinLiang/SSAH-adversarial-attack
공식 코드Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
https://github.com/LinearDesignSoftware/LinearDesign
공식 코드Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning
공식 코드Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
https://github.com/LingmaTongyi/Lingma-SWE-GPT
공식 코드https://github.com/Linvyl/DAM-QA
공식 코드Describe Anything Model for Visual Question Answering on Text-rich Images
https://github.com/Lireanstar/BPDG
공식 코드Bilateral Personalized Dialogue Generation with Contrastive Learning
https://github.com/LittleGuoKe/ConceptFERE
공식 코드https://github.com/LividWo/Perturbed-Masking
공식 코드Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT
https://github.com/Living-with-machines/AtypicalAnimacy
공식 코드https://github.com/Living-with-machines/histLM
공식 코드https://github.com/LiyuanLucasLiu/LD-Net
공식 코드Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
https://github.com/LiyuanLucasLiu/LM-LSTM-CRF
공식 코드Empower Sequence Labeling with Task-Aware Neural Language Model
https://github.com/LiyuanLucasLiu/RAdam
공식 코드https://github.com/Lolo1222/EquivPruner
공식 코드EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning
https://github.com/LooKing9218/RetiZero
공식 코드https://github.com/LoreDema/GePpeTto
공식 코드https://github.com/LoserCheems/OTCE
공식 코드https://github.com/LotusDYH/ssl_robust
공식 코드https://github.com/LuoweiZhou/e2e-gLSTM-sc
공식 코드Watch What You Just Said: Image Captioning with Text-Conditional Attention
https://github.com/M0RJIQUE/tencdm
공식 코드TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings
https://github.com/MAGIC-AI4Med/RaTEScore
공식 코드https://github.com/MALerLab/SejongMusic
공식 코드Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel Encoding
https://github.com/MC-BERT/MC-BERT
공식 코드MC-BERT: Efficient Language Pre-Training via a Meta Controller
https://github.com/METR/ai-rd-tasks
공식 코드RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.