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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5401–5450 of 17610 papers

TitleStatusHype
Accelerating Gossip SGD with Periodic Global Averaging—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review—0
Accelerating Large Language Model Reasoning via Speculative Search—0
Accelerating Large Language Model Training with Hybrid GPU-based Compression—0
Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator—0
Accelerating MoE Model Inference with Expert Sharding—0
Accelerating recurrent neural network language model based online speech recognition system—0
Accelerating Retrieval-Augmented Language Model Serving with Speculation—0
Accessible Instruction-Following Agent—0
AccidentBlip: Agent of Accident Warning based on MA-former—0
Accompanied Singing Voice Synthesis with Fully Text-controlled Melody—0
Accounting for Sycophancy in Language Model Uncertainty Estimation—0
Accuracy Assessment of OpenAlex and Clarivate Scholar ID with an LLM-Assisted Benchmark—0
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination—0
Accurate Word Segmentation using Transliteration and Language Model Projection—0
Accurate, yet Inconsistent? Consistency Analysis on Language Models—0
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language—0
ACER: Automatic Language Model Context Extension via Retrieval—0
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models—0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
A Challenge Set for Advancing Language Modeling—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—0
Achieving Human Parity in Conversational Speech Recognition—0
Achieving Peak Performance for Large Language Models: A Systematic Review—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting—0
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models—0
A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation—0
A Code-Switching Corpus of Turkish-German Conversations—0
Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models—0
A Cognitive Regularizer for Language Modeling—0
A Cohesive Distillation Architecture for Neural Language Models—0
SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users—0
A Combinatorial Identities Benchmark for Theorem Proving via Automated Theorem Generation—0
A Comparative Investigation of Morphological Language Modeling for the Languages of the European Union—0
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model—0
A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation—0
A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation—0
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization—0
A Comparative Study on Patient Language across Therapeutic Domains for Effective Patient Voice Classification in Online Health Discussions—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
A Comparison between Count and Neural Network Models Based on Joint Translation and Reordering Sequences—0
A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts—0
A Comparison of Hybrid and End-to-End Models for Syllable Recognition—0
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese—0
A Comparison of Sentence-Weighting Techniques for NMT—0
A Comparison of Smoothing Techniques for Bilingual Lexicon Extraction from Comparable Corpora—0
A Comparison of Vector-based Representations for Semantic Composition—0
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics—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