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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5501–5550 of 17610 papers

TitleStatusHype
ACtuAL: Actor-Critic Under Adversarial Learning—0
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients—0
AdaGC: Improving Training Stability for Large Language Model Pretraining—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
ADAM-1: AI and Bioinformatics for Alzheimer's Detection and Microbiome-Clinical Data Integrations—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
AdaPrompt: Adaptive Model Training for Prompt-based NLP—0
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax—0
Adaptable Multi-Domain Language Model for Transformer ASR—0
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
Adaptation of Reordering Models for Statistical Machine Translation—0
Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis—0
Adapter Pruning using Tropical Characterization—0
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models—0
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning—0
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications—0
Adapting and evaluating a deep learning language model for clinical why-question answering—0
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives—0
Adapting BigScience Multilingual Model to Unseen Languages—0
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks—0
Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval—0
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift—0
Adapting Large Language Models for Character-based Augmentative and Alternative Communication—0
Adapting Large Language Models to Domains via Reading Comprehension—0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling—0
Adaptive Decoding via Latent Preference Optimization—0
Adaptive Differential Privacy for Language Model Training—0
Adaptive Discounting of Implicit Language Models in RNN-Transducers—0
Adaptive Draft-Verification for Efficient Large Language Model Decoding—0
Adaptively profiling models with task elicitation—0
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling—0
Adaptive Multi-Corpora Language Model Training for Speech Recognition—0
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction—0
Adaptive Noise Injection: A Structure-Expanding Regularization for RNN—0
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training—0
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations—0
Adaptive Reasoning and Acting in Medical Language Agents—0
Adaptive Semantic Prompt Caching with VectorQ—0
Adaptive Semiparametric Language Models—0
Adaptive Testing and Debugging of NLP Models—0
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training—0
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs—0
AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding—0
A Data Efficient End-To-End Spoken Language Understanding Architecture—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