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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9751–9800 of 17610 papers

TitleStatusHype
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—0
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers—0
Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning—0
Masked Vision and Language Modeling for Multi-modal Representation Learning—0
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification—0
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations—0
Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis—0
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining—0
MaskSR: Masked Language Model for Full-band Speech Restoration—0
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation—0
MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models—0
Massively Multilingual Shallow Fusion with Large Language Models—0
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
Mastering Board Games by External and Internal Planning with Language Models—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
MatChat: A Large Language Model and Application Service Platform for Materials Science—0
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching—0
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
Mathematical Information Retrieval based on Type Embeddings and Query Expansion—0
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model—0
MATHion: Solving Math Word Problems with Logically Consistent Problems—0
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration—0
math-PVS: A Large Language Model Framework to Map Scientific Publications to PVS Theories—0
E^2CFD: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model—0
Matrix Is All You Need—0
Matryoshka Multimodal Models—0
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources—0
mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning—0
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model—0
MAVias: Mitigate any Visual Bias—0
Mavors: Multi-granularity Video Representation for Multimodal Large Language Model—0
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models—0
Maximizing Efficiency of Language Model Pre-training for Learning Representation—0
Maximizing Penetration Testing Success with Effective Reconnaissance Techniques using ChatGPT—0
Max-Margin Incremental CCG Parsing—0
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
Exploring the Maze of Multilingual Modeling—0
mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning—0
mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model—0
MCSD: An Efficient Language Model with Diverse Fusion—0
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network—0
MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model—0
Mean-Squared Accuracy of Good-Turing Estimator—0
Measuring an artificial intelligence agent's trust in humans using machine incentives—0
Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning—0
Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.—0
The BS-meter: A ChatGPT-Trained Instrument to Detect Sloppy Language-Games—0
Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings—0
Measuring Feature Sparsity in Language Models—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