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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/xu1998hz/ssl_kba_expert_layman_style_transfer
공식 코드Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
https://github.com/xuandongzhao/crt
공식 코드https://github.com/xuandongzhao/gptwatermark
공식 코드https://github.com/xuandongzhao/hpd
공식 코드Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
https://github.com/xuandongzhao/unigram-watermark
공식 코드https://github.com/xuanlinli17/autoregressive_inference
공식 코드Discovering Non-monotonic Autoregressive Orderings with Variational Inference
https://github.com/xuanlinli17/large_vlm_distillation_ood
공식 코드Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
https://github.com/xuanren4470/i-learn-better-if-you-speak-my-language
공식 코드https://github.com/xud-yan/Trident
공식 코드Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
https://github.com/xueyangfeng/rehac
공식 코드Large Language Model-based Human-Agent Collaboration for Complex Task Solving
https://github.com/xuliu-cyber/rsunivlm
공식 코드https://github.com/xunguangwang/cgat
공식 코드CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval
https://github.com/xunguangwang/instructta
공식 코드InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
https://github.com/xurong-liang/LEGCF
공식 코드https://github.com/xuxiaoxxxx/3DSS-VLG
공식 코드3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
https://github.com/xuyige/BERT4doc-Classification
공식 코드https://github.com/xuzhao0/model-selection-reasoning
공식 코드Automatic Model Selection with Large Language Models for Reasoning
https://github.com/xyease/Dialog-PrLM
공식 코드https://github.com/xyin-anl/Nodeology
공식 코드https://github.com/xyjigsaw/kiscovery
공식 코드Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
https://github.com/xypb/cleft
공식 코드https://github.com/y0mingzhang/diffuse-distributions
공식 코드https://github.com/y0mingzhang/diffuse-probabilities
공식 코드https://github.com/yale-lily/folio
공식 코드https://github.com/yale-nlp/comal
공식 코드COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
https://github.com/yale-sys/confidential-prompting
공식 코드Confidential Prompting: Protecting User Prompts from Cloud LLM Providers
https://github.com/yamanalab/gpt-augmented-news-recommendation
공식 코드News Recommendation with Category Description by a Large Language Model
https://github.com/yan-scnu/prompted_dynamic_detection
공식 코드Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
https://github.com/yanbei-jiang/artwork-interpretation
공식 코드KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graph
https://github.com/yandachen/in-context-tuning
공식 코드https://github.com/yandex-research/DeDLOC
공식 코드https://github.com/yandex-research/btard
공식 코드https://github.com/yandex-research/swarm
공식 코드SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
https://github.com/yandrewl/clape
공식 코드https://github.com/yangcong356/bita
공식 코드Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.