SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8051–8100 of 17610 papers

TitleStatusHype
Improving Large Language Model Fine-tuning for Solving Math Problems—0
Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity—0
Improving Large-scale Paraphrase Acquisition and Generation—0
Improving latent variable descriptiveness with AutoGen—0
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives—0
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Improving Multi-modal Large Language Model through Boosting Vision Capabilities—0
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model—0
Improving Named Entity Transcription with Contextual LLM-based Revision—0
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning—0
Improving Neural Language Generation with Spectrum Control—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
Improving neural morphological Tagging using Language Models—0
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization—0
Improving Non-Autoregressive Translation Models Without Distillation—0
Improving Non-autoregressive Translation Quality with Pretrained Language Model, Embedding Distillation and Upsampling Strategy for CTC—0
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models—0
Improving Phrase-Based SMT Using Cross-Granularity Embedding Similarity—0
Improving Pinterest Search Relevance Using Large Language Models—0
Improving Pivot Translation by Remembering the Pivot—0
Improving the Reusability of Pre-trained Language Models in Real-world Applications—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion—0
Improving Punctuation Restoration for Speech Transcripts via External Data—0
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition—0
Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble—0
Improving reordering performance using higher order and structural features—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
Improving Retrieval Augmented Language Model with Self-Reasoning—0
Improving RNN transducer with normalized jointer network—0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment—0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—0
Improving Scheduled Sampling for Neural Transducer-based ASR—0
Improving Self Consistency in LLMs through Probabilistic Tokenization—0
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model—0
Improving Semantic Parsing for Task Oriented Dialog—0
Improving Short Text Classification With Augmented Data Using GPT-3—0
Improving Significant Wave Height Prediction Using Chronos Models—0
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers—0
Improving Speech Recognition for Indic Languages using Language Model—0
Improving Statistical Machine Translation with a Multilingual Paraphrase Database—0
Improving Statistical Machine Translation with Word Class Models—0
Improving Synonym Recommendation Using Sentence Context—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