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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7051–7100 of 17610 papers

TitleStatusHype
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining—0
SemanticAC: Semantics-Assisted Framework for Audio Classification—0
Semantic approaches to software component retrieval with English queries—0
Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning—0
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance—0
Semantic Communication based on Large Language Model for Underwater Image Transmission—0
Semantic Importance-Aware Communications Using Pre-trained Language Models—0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues—0
Semantic Feature Verification in FLAN-T5—0
Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models—0
Semantic Language Model for Tunisian Dialect—0
Semantic Layered Embedding Diffusion in Large Language Models for Multi-Contextual Consistency—0
Semantic Na\" Bayes Classifier for Document Classification—0
Semantic Parsing as Machine Translation—0
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification—0
Semantic parsing of speech using grammars learned with weak supervision—0
Semantic Preprocessing for LLM-based Malware Analysis—0
Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding—0
Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation—0
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?—0
Semantics-aware Motion Retargeting with Vision-Language Models—0
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR—0
Semantic Structure Extraction for Spreadsheet Tables with a Multi-task Learning Architecture—0
Data Imputation using Large Language Model to Accelerate Recommendation System—0
Semantic Variation in Online Communities of Practice—0
Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval—0
SeMe: Training-Free Language Model Merging via Semantic Alignment—0
Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis—0
SemEval-2025 Task 9: The Food Hazard Detection Challenge—0
Semformer: Transformer Language Models with Semantic Planning—0
SemGrasp: Semantic Grasp Generation via Language Aligned Discretization—0
Semi-Autoregressive Streaming ASR With Label Context—0
Semiparametric Language Models Are Scalable Continual Learners—0
Semi-Parametric Video-Grounded Text Generation—0
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
Semi-supervised acoustic model training for five-lingual code-switched ASR—0
Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation—0
Semi-supervised Contextual Historical Text Normalization—0
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data—0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data—0
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations—0
Self-Supervised Image Captioning with CLIP—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition—0
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports—0
Semi-Supervised Neural Machine Translation with Language Models—0
Semi-Supervised Technical Term Tagging With Minimal User Feedback—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