SOTAVerified

Language Modelling

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Papers

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Showing 9701–9750 of 17610 papers

TitleStatusHype
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Making a Computational Attorney—0
Making Convolutional Networks Recurrent for Visual Sequence Learning—0
Making first order linear logic a generating grammar—0
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation—0
Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section—0
Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals—0
MaLA-500: Massive Language Adaptation of Large Language Models—0
mALBERT: Is a Compact Multilingual BERT Model Still Worth It?—0
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation—0
Malicious Path Manipulations via Exploitation of Representation Vulnerabilities of Vision-Language Navigation Systems—0
MaLLaM -- Malaysia Large Language Model—0
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data—0
MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation—0
MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation—0
MambaByte: Token-free Selective State Space Model—0
MammothModa: Multi-Modal Large Language Model—0
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation—0
Manipulating the Label Space for In-Context Classification—0
Manipulation and the AI Act: Large Language Model Chatbots and the Danger of Mirrors—0
MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling—0
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders—0
Many-Shot Regurgitation (MSR) Prompting—0
Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model—0
MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model—0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models—0
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation—0
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization—0
Mapping Brains with Language Models: A Survey—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model—0
Mapping Researcher Activity based on Publication Data by means of Transformers—0
Mapping Rules for Building a Tunisian Dialect Lexicon and Generating Corpora—0
Mapping the Timescale Organization of Neural Language Models—0
MapQA: Open-domain Geospatial Question Answering on Map Data—0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
MARCO: Multi-Agent Real-time Chat Orchestration—0
Marconi: Prefix Caching for the Era of Hybrid LLMs—0
Maritime Mission Planning for Unmanned Surface Vessel using Large Language Model—0
Markov Constraint as Large Language Model Surrogate—0
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity—0
MARS6: A Small and Robust Hierarchical-Codec Text-to-Speech Model—0
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer—0
Mask and Regenerate: A Classifier-based Approach for Unpaired Sentiment Transformation of Reviews for Electronic Commerce Websites.—0
MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow—0
Masked Adversarial Generation for Neural Machine Translation—0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling—0
Masked ELMo: An evolution of ELMo towards fully contextual RNN language models—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified