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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14351–14400 of 17610 papers

TitleStatusHype
Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models—0
Intrinsic Tensor Field Propagation in Large Language Models: A Novel Approach to Contextual Information Flow—0
Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis—0
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language—0
Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task—0
Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew—0
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions—0
Introducing Semantics into Speech Encoders—0
Introduction: Cognitive Issues in Natural Language Processing—0
Introduction of a Probabilistic Language Model to Non-Factoid Question Answering Using Example Q\&A Pairs—0
Introduction to CKIP Chinese Spelling Check System for SIGHAN Bakeoff 2013 Evaluation—0
Introspective Tips: Large Language Model for In-Context Decision Making—0
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
Intuitionistic Fuzzy Sets for Large Language Model Data Annotation: A Novel Approach to Side-by-Side Preference Labeling—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting—0
Invariant Language Modeling—0
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation—0
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities—0
Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning—0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation—0
Investigating Compositional Reasoning in Time Series Foundation Models—0
Investigating Continuous Space Language Models for Machine Translation Quality Estimation—0
Investigating Data Contamination for Pre-training Language Models—0
Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus—0
Investigating Learning Dynamics of BERT Fine-Tuning—0
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation—0
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets—0
Investigating Masking-based Data Generation in Language Models—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models—0
探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]—0
Investigating on RLHF methodology—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
Investigating Speech Recognition for Improving Predictive AAC—0
Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation—0
Investigating the Catastrophic Forgetting in Multimodal Large Language Models—0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions—0
Effects of sub-word segmentation on performance of transformer language models—0
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
Investigating the Impact of Text Summarization on Topic Modeling—0
Investigating the Impact of Word Informativeness on Speech Emotion Recognition—0
Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study—0
Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training—0
Investigating the Timescales of Language Processing with EEG and Language Models—0
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition—0
Investigating Vision-Language Model for Point Cloud-based Vehicle Classification—0
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language—0
Investigation of Large-Margin Softmax in Neural Language Modeling—0
Show:102550
← PrevPage 288 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