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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9751–9800 of 17610 papers

TitleStatusHype
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition—0
Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval—0
Unsupervised Multi-View Post-OCR Error Correction With Language Models—0
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation—0
What Syntactic Structures block Dependencies in RNN Language Models?—0
Zyda-2: a 5 Trillion Token High-Quality Dataset—0
What the [MASK]? Making Sense of Language-Specific BERT Models—0
Word Ordering with Phrase-Based Grammars—0
Unsupervised Natural Question Answering with a Small Model—0
Unsupervised neural and Bayesian models for zero-resource speech processing—0
Unsupervised Neural Machine Translation with Generative Language Models Only—0
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook—0
Unsupervised Paraphrasability Prediction for Compound Nominalizations—0
Unsupervised Paraphrasing with Pretrained Language Models—0
Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM—0
Unsupervised Prediction of Acceptability Judgements—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Unsupervised Pretraining for Sequence to Sequence Learning—0
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion—0
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion—0
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement—0
Unsupervised Semantic Role Induction with Global Role Ordering—0
Unsupervised Inflection Generation Using Neural Language Modeling—0
Unified Text Structuralization with Instruction-tuned Language Models—0
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription—0
Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval—0
Unsupervised Text Style Transfer with Content Embeddings—0
Unsupervised training of maximum-entropy models for lexical selection in rule-based machine translation—0
Uniform Masking Prevails in Vision-Language Pretraining—0
Unsupervised Vocabulary Adaptation for Morph-based Language Models—0
Learning to Discover, Ground and Use Words with Segmental Neural Language Models—0
Unsupervised Word Discovery with Segmental Neural Language Models—0
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks—0
Unsupervised word segmentation and lexicon discovery using acoustic word embeddings—0
Unifying Corroborative and Contributive Attributions in Large Language Models—0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation—0
Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization—0
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons—0
Unveiling Disparities in Maternity Care: A Topic Modelling Approach to Analysing Maternity Incident Investigation Reports—0
Zero-shot cross-lingual transfer in instruction tuning of large language models—0
UniCoder: Scaling Code Large Language Model via Universal Code—0
Unifying Input and Output Smoothing in Neural Machine Translation—0
Unveiling Hidden Links Between Unseen Security Entities—0
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model—0
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability—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