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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10001–10050 of 17610 papers

TitleStatusHype
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation—0
MMSummary: Multimodal Summary Generation for Fetal Ultrasound Video—0
μnit Scaling: Simple and Scalable FP8 LLM Training—0
MNN-LLM: A Generic Inference Engine for Fast Large Language Model Deployment on Mobile Devices—0
MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment—0
MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents—0
MobileFlow: A Multimodal LLM For Mobile GUI Agent—0
Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach—0
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering—0
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks—0
MoChat: Joints-Grouped Spatio-Temporal Grounding LLM for Multi-Turn Motion Comprehension and Description—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
Model and Evaluation: Towards Fairness in Multilingual Text Classification—0
Model-as-a-Service (MaaS): A Survey—0
Model-based Large Language Model Customization as Service—0
Model Card and Evaluations for Claude Models—0
Model Combination for Correcting Preposition Selection Errors—0
Model-Enhanced LLM-Driven VUI Testing of VPA Apps—0
Modeling Child Divergences from Adult Grammar—0
Modeling Code-Switch Languages Using Bilingual Parallel Corpus—0
Modeling Concept Dependencies in a Scientific Corpus—0
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences—0
Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients—0
Modeling Event Salience in Narratives via Barthes' Cardinal Functions—0
Modeling fMRI time courses with linguistic structure at various grain sizes—0
Neural language modeling of free word order argument structure—0
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language—0
Modeling infant segmentation of two morphologically diverse languages—0
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions—0
Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks—0
Modeling Long Context for Task-Oriented Dialogue State Generation—0
Modeling Mathematical Notation Semantics in Academic Papers—0
Modeling Northern Haida Verb Morphology—0
Modeling of term-distance and term-occurrence information for improving n-gram language model performance—0
Modeling Order in Neural Word Embeddings at Scale—0
HPC-Coder: Modeling Parallel Programs using Large Language Models—0
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation—0
Modeling Structural Topic Transitions for Automatic Lyrics Generation—0
Modeling structure-building in the brain with CCG parsing and large language models—0
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities—0
Modeling with Recurrent Neural Networks for Open Vocabulary Slots—0
Modeling Word Meaning in Context with Substitute Vectors—0
Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation—0
Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception—0
Modelling Political Coalition Negotiations Using LLM-based Agents—0
Modelling selectional preferences in a lexical hierarchy—0
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC—0
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection—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