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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/cbhu523/chatgpt_ASD_diagnosis
공식 코드https://github.com/cbjuan/tokenizers-neural-nets-2020-paper
공식 코드https://github.com/cbluebenchmark/cblue
공식 코드CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
https://github.com/cccccent/eg3p
공식 코드Explanation Graph Generation via Generative Pre-training over Synthetic Graphs
https://github.com/cciiplab/dpt
공식 코드Declaration-based Prompt Tuning for Visual Question Answering
https://github.com/cctien/bimultialign
공식 코드Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining
https://github.com/cdmalon/finetune-transformer-lm
공식 코드https://github.com/cedricdeboom/RepresentationLearning
공식 코드Representation learning for very short texts using weighted word embedding aggregation
https://github.com/ceeai/elle
공식 코드https://github.com/ceezh/llovi
공식 코드A Simple LLM Framework for Long-Range Video Question-Answering
https://github.com/celbree/reacc
공식 코드https://github.com/cemuluoglakci/hypotermqa
공식 코드HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs
https://github.com/cerebras/modelzoo
공식 코드BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
https://github.com/cerebras/online-normalization
공식 코드https://github.com/cerebrasresearch/nanogns
공식 코드https://github.com/cestlucas/rate
공식 코드RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap
https://github.com/cewu/ptnl
공식 코드Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
https://github.com/cgnorthcutt/forum-diversification
공식 코드https://github.com/cgraywang/deepex
공식 코드Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
https://github.com/cgraywang/deepstruct
공식 코드DeepStruct: Pretraining of Language Models for Structure Prediction
https://github.com/cgraywang/gluon-nlp-1
공식 코드https://github.com/cgshpi/topic-models-and-dimensionality-reduction-benchmark
공식 코드https://github.com/chaeyoon-jang/bomf
공식 코드Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
https://github.com/chaitanyamalaviya/expertqa
공식 코드https://github.com/chancefocus/pixiu
공식 코드PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
https://github.com/chancharikmitra/ccot
공식 코드Compositional Chain-of-Thought Prompting for Large Multimodal Models
https://github.com/chandar-lab/amplify
공식 코드https://github.com/chandar-lab/cmoptimizer
공식 코드Promoting Exploration in Memory-Augmented Adam using Critical Momenta
https://github.com/chandar-lab/efficientllms
공식 코드Exploring Quantization for Efficient Pre-Training of Transformer Language Models
https://github.com/chandar-lab/fasp
공식 코드https://github.com/chandar-lab/subgoal_distillation_llm
공식 코드Sub-goal Distillation: A Method to Improve Small Language Agents
https://github.com/chang-github-00/llm-predictive-decoding
공식 코드Non-myopic Generation of Language Models for Reasoning and Planning
https://github.com/changyuanwang17/qvlm
공식 코드Q-VLM: Post-training Quantization for Large Vision-Language Models
https://github.com/chantalmp/radialog
공식 코드https://github.com/chantalmp/unsupervised_pre-training_of_graph_transformers_on_patient_population_graphs
공식 코드Unsupervised pre-training of graph transformers on patient population graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.