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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5451–5500 of 17610 papers

TitleStatusHype
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?—0
A Complex Network based Graph Embedding Method for Link Prediction—0
A Compositional Approach to Language Modeling—0
A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models—0
A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias—0
A Comprehensive Approach to Misspelling Correction with BERT and Levenshtein Distance—0
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR—0
A Comprehensive Survey of Grammar Error Correction—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation—0
A Constraint-Enforcing Reward for Adversarial Attacks on Text Classifiers—0
A Contemporary Survey of Large Language Model Assisted Program Analysis—0
A Context Free TAG Variant—0
A Contextual Language Model to Improve Machine Translation of Pronouns by Re-ranking Translation Hypotheses—0
A Continuous Space Neural Language Model for Bengali Language—0
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning—0
A Conventional Orthography for Tunisian Arabic—0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation—0
A Cost Efficient Approach to Correct OCR Errors in Large Document Collections—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding—0
Acoustic Model Fusion for End-to-end Speech Recognition—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR—0
Acoustic-to-Word Recognition with Sequence-to-Sequence Models—0
Acquiring distributed representations for verb-object pairs by using word2vec—0
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction—0
Acquiring Linguistic Knowledge from Multimodal Input—0
A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
A Cross-language Study on Automatic Speech Disfluency Detection—0
Acrostic Poem Generation—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Action Verb Corpus—0
Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation—0
Activation Scaling for Steering and Interpreting Language Models—0
Combing for Credentials: Active Pattern Extraction from Smart Reply—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation—0
Active Learning with Tabular Language Models—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Active Preference Learning for Large Language Models—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling—0
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference—0
ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation—0
Actor-Critic based Online Data Mixing For Language Model Pre-Training—0
Show:102550
← PrevPage 110 of 353Next →
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