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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8851–8900 of 17610 papers

TitleStatusHype
Language Modelling Approaches to Adaptive Machine Translation—0
Language Modelling as a Multi-Task Problem—0
Language Modelling for Speaker Diarization in Telephonic Interviews—0
Language modelling techniques for analysing the impact of human genetic variation—0
Language Modelling via Learning to Rank—0
Language Modelling with NMT Query Translation for Amharic-Arabic Cross-Language Information Retrieval—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Language Model Metrics and Procrustes Analysis for Improved Vector Transformation of NLP Embeddings—0
Language Model Personalization via Reward Factorization—0
Language Model Pretraining and Transfer Learning for Very Low Resource Languages—0
Language Model Pre-training for Hierarchical Document Representations—0
Language Model Pre-training Improves Generalization in Policy Learning—0
Language Model Pre-training on True Negatives—0
Language Model Priming for Cross-Lingual Event Extraction—0
Language Model Prompt Selection via Simulation Optimization—0
Language Model Re-rankers are Steered by Lexical Similarities—0
Language Model Rest Costs and Space-Efficient Storage—0
Language Models: A Guide for the Perplexed—0
Language models and brains align due to more than next-word prediction and word-level information—0
Language Models are General-Purpose Interfaces—0
Language Models are Good Translators—0
Large Language Models are not Models of Natural Language: they are Corpus Models—0
Language Models are Symbolic Learners in Arithmetic—0
Language models are weak learners—0
Language Models as a Knowledge Source for Cognitive Agents—0
Language Models as Emotional Classifiers for Textual Conversations—0
Language Models as Fact Checkers?—0
Language Models can be Logical Solvers—0
Language Model Self-improvement by Reinforcement Learning Contemplation—0
Language Models for Cloze Task Answer Generation in Russian—0
Language Models for Image Captioning: The Quirks and What Works—0
Language Models for Machine Translation: Original vs. Translated Texts—0
Language Models for Novelty Detection in System Call Traces—0
Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions—0
Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More—0
Language Models "Grok" to Copy—0
Language models in molecular discovery—0
Language Models Learn POS First—0
Language models of protein sequences at the scale of evolution enable accurate structure prediction—0
Language Models of Spoken Dutch—0
Language Models sounds the Death Knell of Knowledge Graphs—0
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion—0
Language Model Supervision for Handwriting Recognition Model Adaptation—0
Language Models Use Trigonometry to Do Addition—0
Language Models with Conformal Factuality Guarantees—0
Language Production Dynamics with Recurrent Neural Networks—0
Language-Queried Target Sound Extraction Without Parallel Training Data—0
Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
Language Resources for Dutch Large Language Modelling—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