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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13251–13300 of 17610 papers

TitleStatusHype
Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt—0
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining—0
Syncretism and How to Deal with it in a Morphological Analyzer: a German Example—0
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages—0
SynDL: A Large-Scale Synthetic Test Collection for Passage Retrieval—0
Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems—0
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling—0
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design—0
Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems—0
Synocene, Beyond the Anthropocene: De-Anthropocentralising Human-Nature-AI Interaction—0
Synslator: An Interactive Machine Translation Tool with Online Learning—0
Syntactically Guided Neural Machine Translation—0
Syntactic and Lexical Complexity in Italian Noncanonical Structures—0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Syntactic Learnability of Echo State Neural Language Models at Scale—0
Syntactic Relevance XLNet Word Embedding Generation in Low-Resource Machine Translation—0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders—0
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification—0
Syntax-Aware Language Modeling with Recurrent Neural Networks—0
Syntax-based Multi-system Machine Translation—0
Syntax-Based Word Ordering Incorporating a Large-Scale Language Model—0
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation—0
Syntax-guided Contrastive Learning for Pre-trained Language Model—0
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis—0
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models—0
Syntax-informed Question Answering with Heterogeneous Graph Transformer—0
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets—0
Synthesising Sign Language from semantics, approaching "from the target and back"—0
Synthesizer: Rethinking Self-Attention for Transformer Models—0
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications—0
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search—0
Synthetic Cross-language Information Retrieval Training Data—0
Synthetic Fluency: Hallucinations, Confabulations, and the Creation of Irish Words in LLM-Generated Translations—0
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters—0
Synthetic Literature: Writing Science Fiction in a Co-Creative Process—0
Synthetic Multimodal Question Generation—0
Synthetic Propaganda Embeddings To Train A Linear Projection—0
Systematicity in GPT-3's Interpretation of Novel English Noun Compounds—0
System Combination of RBMT plus SPE and Preordering plus SMT—0
System description for ProfNER - SMMH: Optimized finetuning of a pretrained transformer and word vectors—0
System of Agentic AI for the Discovery of Metal-Organic Frameworks—0
Pushing the Boundaries of Text to Motion with Arbitrary Text: A New Task—0
T\"UB\.ITAK SMT System Submission for WMT2016—0
Tabby: Tabular Data Synthesis with Language Models—0
Tables to LaTeX: structure and content extraction from scientific tables—0
Table-to-Text: Describing Table Region with Natural Language—0
Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization—0
TACO: Pre-training of Deep Transformers with Attention Convolution using Disentangled Positional Representation—0
Tactical Decision for Multi-UGV Confrontation with a Vision-Language Model-Based Commander—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