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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/j-l-o/sk-hurt-ncd
공식 코드Supervised Knowledge May Hurt Novel Class Discovery Performance
https://github.com/j-min/VL-T5
공식 코드https://github.com/j-min/dalleval
공식 코드DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
https://github.com/jLooo/VADMamba
공식 코드VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
https://github.com/jaaack-wang/linguistic-knowledge-in-DA-for-NLP
공식 코드https://github.com/jabalazs/gating
공식 코드Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
https://github.com/jackil1993/gpt3_scm
공식 코드https://github.com/jackingchen/reason_augment_ad_detect
공식 코드https://github.com/jacklishufan/lavida
공식 코드LaViDa: A Large Diffusion Language Model for Multimodal Understanding
https://github.com/jacklxc/StandAloneSpellingCorrection
공식 코드https://github.com/jackroos/VL-BERT
공식 코드VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/jacksonwuxs/interpret_instruction_tuning_llms
공식 코드https://github.com/jacksonwuxs/noppa
공식 코드NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation
https://github.com/jacksonwuxs/promptrec
공식 코드Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
https://github.com/jacob-parnell-rozetta/sumtra
공식 코드SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization
https://github.com/jacobandreas/geca
공식 코드https://github.com/jacobfa/fft
공식 코드SPECTRE: An FFT-Based Efficient Drop-In Replacement to Self-Attention for Long Contexts
https://github.com/jacobyhsi/InterpreTabNet
공식 코드InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
https://github.com/jacoxu/short-text-hashing-HMTT
공식 코드Short Text Hashing Improved by Integrating Multi-Granularity Topics and Tags
https://github.com/jadeleiyu/lm_hallucination_mechanisms
공식 코드Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations
https://github.com/jadeleiyu/sworme
공식 코드https://github.com/jadeleiyu/word_sense_extension
공식 코드https://github.com/jaeseokbyun/grit-vlp
공식 코드GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training
https://github.com/jaeyeonkim99/enclap
공식 코드EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning
https://github.com/jaidoshi/knowledge-erasure
공식 코드Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods
https://github.com/jaiprakash1824/vlm_adv_attack
공식 코드https://github.com/jakespringer/echo-embeddings
공식 코드https://github.com/jakpra/l2-prepositions
공식 코드Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model
https://github.com/jakpra/linguisticstructurelm
공식 코드Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling
https://github.com/jakpra/sufficiencylowerbounds
공식 코드https://github.com/jam963/semantics-or-spelling
공식 코드Semantics or spelling? Probing contextual word embeddings with orthographic noise
https://github.com/jameslahm/scpnet
공식 코드Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
https://github.com/jamespark3922/localized-skd
공식 코드Localized Symbolic Knowledge Distillation for Visual Commonsense Models
https://github.com/jamespark3922/lskd
공식 코드Localized Symbolic Knowledge Distillation for Visual Commonsense Models
https://github.com/jangerritha/CycleIK
공식 코드Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.