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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/skyriver-2000/trad-official
공식 코드TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
https://github.com/skyuforever/cre-llm
공식 코드https://github.com/skyworkai/skywork
공식 코드https://github.com/slab-nlp/akk
공식 코드Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach
https://github.com/slash0bz/speaker-identification
공식 코드Cross-Lingual Speaker Identification Using Distant Supervision
https://github.com/slavaheroes/brain-captioning-with-gpt2
공식 코드Decoding fMRI Data into Captions using Prefix Language Modeling
https://github.com/sled-group/TnD
공식 코드Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations
https://github.com/sled-group/chat-with-nerf
공식 코드LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent
https://github.com/sled-group/driVLMe
공식 코드DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences
https://github.com/sled-group/world-to-words
공식 코드World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models
https://github.com/slp-rl/salmon
공식 코드https://github.com/slp-rl/slamkit
공식 코드Slamming: Training a Speech Language Model on One GPU in a Day
https://github.com/slp-rl/slm-discrete-representations
공식 코드Analysing Discrete Self Supervised Speech Representation for Spoken Language Modeling
https://github.com/slp-rl/spokenstorycloze
공식 코드https://github.com/slytheringe/rsteller
공식 코드https://github.com/sm354/mmgn
공식 코드IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection
https://github.com/smallbenchnlp/benchmark
공식 코드Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing
https://github.com/smartandcleverrobot/icra-cirn
공식 코드Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network
https://github.com/smartmediaai/llm_recsys
공식 코드https://github.com/smartmediaai/lmrs
공식 코드https://github.com/smartschat/cort
공식 코드https://github.com/smartyfh/MultiWOZ2.4
공식 코드https://github.com/smartyfh/infocqr
공식 코드Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
https://github.com/smilni/semantic_leakage_project
공식 코드Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller Language Models
https://github.com/sming256/bolt
공식 코드BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
https://github.com/snipsco/automatic-data-generation
공식 코드Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems
https://github.com/snoels/fingeit
공식 코드https://github.com/snorkel-ai/long-context-eval
공식 코드Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time Correction
https://github.com/snowflakedb/arcticinference
공식 코드SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
https://github.com/snowteam2023/medrag
공식 코드A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning
https://github.com/snowyj/sem_syn_separation
공식 코드Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
https://github.com/snu-mllab/bayesian-red-teaming
공식 코드Query-Efficient Black-Box Red Teaming via Bayesian Optimization
https://github.com/snu-mllab/context-memory
공식 코드Compressed Context Memory For Online Language Model Interaction
https://github.com/snu-mllab/guidedquant
공식 코드GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
https://github.com/snudm-starlab/k-prune
공식 코드Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.