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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/anoopkdcs/redaffectivelm
공식 코드https://github.com/anosorae/irra
공식 코드Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
https://github.com/anpoc/language-models-in-medicine
공식 코드Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
https://github.com/ans92/tlac
공식 코드TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification
https://github.com/ant-8/GUI-Grounding-via-Iterative-Narrowing
공식 코드https://github.com/ant-8/layer-recurrent-transformers
공식 코드Intra-Layer Recurrence in Transformers for Language Modeling
https://github.com/ant-research/MagicQuill
공식 코드https://github.com/ant-research/easytemporalpointprocess
공식 코드Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
https://github.com/ant-research/fin_domain_llm
공식 코드https://github.com/ant-research/structuredlm_rtdt
공식 코드https://github.com/anthipapa/textanonymization
공식 코드Bootstrapping Text Anonymization Models with Distant Supervision
https://github.com/anthonywchen/rarr
공식 코드RARR: Researching and Revising What Language Models Say, Using Language Models
https://github.com/anthropics/evals
공식 코드Discovering Language Model Behaviors with Model-Written Evaluations
https://github.com/anthropics/hh-rlhf
공식 코드Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
https://github.com/anthropics/sycophancy-to-subterfuge-paper
공식 코드Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
https://github.com/antnlp/nope_head_scale
공식 코드Length Generalization of Causal Transformers without Position Encoding
https://github.com/antoinelrnld/modlog
공식 코드MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic
https://github.com/antonio-mastropaolo/satd-removal
공식 코드Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
https://github.com/antonschafer/duplicate-subwords
공식 코드On the Effect of (Near) Duplicate Subwords in Language Modelling
https://github.com/antonschafer/xling-imbalance
공식 코드https://github.com/antoyang/FrozenBiLM
공식 코드Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
https://github.com/anudeex/wet
공식 코드https://github.com/anusfoil/llaqo
공식 코드LLaQo: Towards a Query-Based Coach in Expressive Music Performance Assessment
https://github.com/anzeyimana/kinyabert-acl2022
공식 코드https://github.com/ao1neko/Heuristic-and-Rational-Reasoning
공식 코드First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
https://github.com/aoshuang92/css_uq_llms
공식 코드CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs
https://github.com/aounon/certified-llm-safety
공식 코드https://github.com/apartresearch/seqcont_circuits
공식 코드Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models
https://github.com/apcl-research/funcom-useloss
공식 코드Semantic Similarity Loss for Neural Source Code Summarization
https://github.com/apcl-research/jam
공식 코드A Language Model of Java Methods with Train/Test Deduplication
https://github.com/apmoore1/language-model
공식 코드Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
https://github.com/appl-team/appl
공식 코드https://github.com/apple/corenet
공식 코드OpenELM: An Efficient Language Model Family with Open Training and Inference Framework
https://github.com/apple/dmel
공식 코드https://github.com/apple/ml-ademamix
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.