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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7751–7800 of 17610 papers

TitleStatusHype
Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE—0
Technical Report: Small Language Model for Japanese Clinical and Medicine—0
Technology Mapping Using WebAI: The Case of 3D Printing—0
TED: Accelerate Model Training by Internal Generalization—0
TED-LIUM: an Automatic Speech Recognition dedicated corpus—0
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models—0
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence—0
Tele-FLM Technical Report—0
Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise—0
Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted—0
Telugu OCR Framework using Deep Learning—0
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling—0
Template-Free Construction of Rhyming Poems with Thematic Cohesion—0
Temporal classification for historical Romanian texts—0
Temporal Common Sense Acquisition with Minimal Supervision—0
Temporal Language Modeling for Short Text Document Classification with Transformers—0
Temporal Modelling of Geospatial Words in Twitter—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
TensorAR: Refinement is All You Need in Autoregressive Image Generation—0
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling—0
Tensorized Transformer for Dynamical Systems Modeling—0
Tensor network language model—0
TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing—0
Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting—0
TernaryLLM: Ternarized Large Language Model—0
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization—0
Test Code Generation for Telecom Software Systems using Two-Stage Generative Model—0
Testing and Evaluation of Large Language Models: Correctness, Non-Toxicity, and Fairness—0
Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems—0
Testing Language Model Agents Safely in the Wild—0
Testing learning hypotheses using neural networks by manipulating learning data—0
Testing the Effect of Code Documentation on Large Language Model Code Understanding—0
Testing the Processing Hypothesis of word order variation using a probabilistic language model—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling—0
Test-Time Adaptation for Visual Document Understanding—0
Test Time Learning for Time Series Forecasting—0
Test-Time Training Provably Improves Transformers as In-context Learners—0
t-Exponential Memory Networks for Question-Answering Machines—0
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model—0
Text-aware and Context-aware Expressive Audiobook Speech Synthesis—0
Text-Based Action-Model Acquisition for Planning—0
Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging—0
Text-based Person Search without Parallel Image-Text Data—0
TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model—0
Textbooks Are All You Need—0
Text Classification of Cancer Clinical Trial Eligibility Criteria—0
Text Clustering with Large Language Model Embeddings—0
Text Completion using Context-Integrated Dependency Parsing—0
Text Compression for Efficient Language Generation—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