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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/krr-oxford/lm-ontology-concept-placement
공식 코드A Language Model based Framework for New Concept Placement in Ontologies
https://github.com/krr-oxford/oet
공식 코드Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
https://github.com/krzentner/language-world
공식 코드Conditionally Combining Robot Skills using Large Language Models
https://github.com/ktlktl/c-llm
공식 코드C-LLM: Learn to Check Chinese Spelling Errors Character by Character
https://github.com/ktlktl/eval-gcsc
공식 코드Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction
https://github.com/ku-nlp/steganography-with-masked-lm
공식 코드Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
https://github.com/kuanchihhuang/reason3d
공식 코드Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
https://github.com/kuleens/vlmbiaseval
공식 코드debiaSAE: Benchmarking and Mitigating Vision-Language Model Bias
https://github.com/kuleshov-group/bd3lms
공식 코드Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
https://github.com/kuleshov-group/mdlm
공식 코드https://github.com/kulinshah98/llm-reasoning-logic-puzzles
공식 코드Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles
https://github.com/kumarmanas/SHIFT
공식 코드Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
https://github.com/kundajelab/dart-eval
공식 코드DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
https://github.com/kunkunlin1221/InstructFLIP
공식 코드InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
https://github.com/kuribayashi4/surprisal_reading_time_en_ja
공식 코드https://github.com/kushalchauhan98/ticket-segmentation
공식 코드https://github.com/kushaltatariya/Pixology
공식 코드Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models
https://github.com/kvablack/ddpo-pytorch
공식 코드https://github.com/kwang2049/pytorch-bertflow
공식 코드https://github.com/kwang2049/useb
공식 코드https://github.com/kxplaug/taa-bench
공식 코드https://github.com/kylezheng1997/simmatch
공식 코드https://github.com/kylrth/procedure-generation
공식 코드Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
https://github.com/kyunghyuncho/bert-gen
공식 코드BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
https://github.com/kyutai-labs/moshi
공식 코드Moshi: a speech-text foundation model for real-time dialogue
https://github.com/kzl/decision-transformer
공식 코드Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/l1277471578/hybrid-emba3d
공식 코드https://github.com/l3cube-pune/MarathiNLP
공식 코드L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library
https://github.com/l3cube-pune/code-mixed-nlp
공식 코드L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models
https://github.com/lab-v2/chatgpt_mwp_eval
공식 코드An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)
https://github.com/lab-v2/diversity_measures
공식 코드Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
https://github.com/lab123-tech/gstran
공식 코드GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation
https://github.com/laekov/fastmoe
공식 코드https://github.com/lagom-nlp/transtokenizer
공식 코드https://github.com/laihaoran/carzero
공식 코드CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.