Papers with Code 아카이브
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/bernard-yang/cmn-acl2023
공식 코드https://github.com/bethelmelesse/unifiedcrawl
공식 코드UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs on Low-Resource Languages
https://github.com/bethxx99/FinTruthQA
공식 코드FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure
https://github.com/better-chao/perceptual_abilities_evaluation
공식 코드Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation
https://github.com/betterzh/sevlm-code
공식 코드Training A Small Emotional Vision Language Model for Visual Art Comprehension
https://github.com/betterzh/sevlm-for-visual-art-comprehension
공식 코드Training A Small Emotional Vision Language Model for Visual Art Comprehension
https://github.com/beyondguo/STA
공식 코드Selective Text Augmentation with Word Roles for Low-Resource Text Classification
https://github.com/beyondguo/genius
공식 코드https://github.com/beyondhtx/qprompt
공식 코드Quantized Prompt for Efficient Generalization of Vision-Language Models
https://github.com/bezirganyan/luma
공식 코드LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
https://github.com/bfarzin/haha_2019_final
공식 코드Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction
https://github.com/bflameswift/uni-mumer
공식 코드https://github.com/bflashcp3f/deer
공식 코드Label-Guided In-Context Learning for Named Entity Recognition
https://github.com/bhattarailab/pathfinder-generator
공식 코드Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation
https://github.com/bhattg/Decay-RNN-ACL-SRW2020
공식 코드How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
https://github.com/bhigy/zr-2021vg_baseline
공식 코드ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition
https://github.com/bhui97/pleak
공식 코드PLeak: Prompt Leaking Attacks against Large Language Model Applications
https://github.com/bias-barometer/birth-of-bias
공식 코드The Birth of Bias: A case study on the evolution of gender bias in an English language model
https://github.com/bids-xu-lab/biomedical-nlp-benchmarks
공식 코드https://github.com/bids-xu-lab/clinical_entity_recognition_using_gpt_models
공식 코드Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
https://github.com/bigai-nlco/docgnre
공식 코드https://github.com/bigai-nlco/lstp-chat
공식 코드Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge
https://github.com/bigai-nlco/videotgb
공식 코드Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge
https://github.com/bigheiniu/grenade
공식 코드https://github.com/bigheiniu/mend
공식 코드MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
https://github.com/bigscience-workshop/Megatron-DeepSpeed
공식 코드What Language Model to Train if You Have One Million GPU Hours?
https://github.com/bigscience-workshop/architecture-objective
공식 코드What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
https://github.com/bigscience-workshop/biomedical
공식 코드BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
https://github.com/bigscience-workshop/carbon-footprint
공식 코드Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
https://github.com/bigscience-workshop/multilingual-modeling
공식 코드BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting
https://github.com/bigscience-workshop/promptsource
공식 코드Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/bigscience-workshop/t-zero
공식 코드Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/bigvideoresearch/Enigma
공식 코드https://github.com/bilaliman/rv_explainability
공식 코드Generating Zero-shot Abstractive Explanations for Rumour Verification
https://github.com/bilel-bj/rosgpt_vision
공식 코드ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts
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