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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12201–12250 of 17610 papers

TitleStatusHype
WMT 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks—0
WoNeF, an improved, expanded and evaluated automatic French translation of WordNet—0
Zero-shot Object Navigation with Vision-Language Models Reasoning—0
WordAlchemy: A transformer-based Reverse Dictionary—0
Word Alignment as Preference for Machine Translation—0
Word Alignment without NULL Words—0
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models—0
Word-based Domain Adaptation for Neural Machine Translation—0
Word Class Based Language Modeling: A Case of Upper Sorbian—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Unlocking Video-LLM via Agent-of-Thoughts Distillation—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Word Importance Explains How Prompts Affect Language Model Outputs—0
Word-Level Representation From Bytes For Language Modeling—0
Word Midas Powered by StringNet: Discovering Lexicogrammatical Constructions in Situ—0
Training-Free Action Recognition and Goal Inference with Dynamic Frame Selection—0
Word Ordering with Phrase-Based Grammars—0
Word Order Matters when you Increase Masking—0
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection—0
Word Play for Playing Othello (Reverses)—0
Word Sense Disambiguation Improves Information Retrieval—0
Unified Multi-Criteria Chinese Word Segmentation with BERT—0
Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization—0
Word Sense Induction with Knowledge Distillation from BERT—0
Word Sketches for Turkish—0
Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective—0
Word surprisal predicts N400 amplitude during reading—0
Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks—0
Word Vector/Conditional Random Field-based Chinese Spelling Error Detection for SIGHAN-2015 Evaluation—0
Unlocking the Potential of Model Merging for Low-Resource Languages—0
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data—0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions—0
World Models: The Safety Perspective—0
World Models with Hints of Large Language Models for Goal Achieving—0
Unlocking Spatial Comprehension in Text-to-Image Diffusion Models—0
Unifying Multitrack Music Arrangement via Reconstruction Fine-Tuning and Efficient Tokenization—0
Worldwide Federated Training of Language Models—0
Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval—0
Zyda-2: a 5 Trillion Token High-Quality Dataset—0
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding—0
Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews—0
Zero-Shot Question Answering over Financial Documents using Large Language Models—0
WSD for n-best reranking and local language modeling in SMT—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist—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