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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12201–12250 of 17610 papers

TitleStatusHype
Dual Mechanism Priming Effects in Hindi Word Order—0
Cloning Ideology and Style using Deep Learning—0
Differentially Private Language Models for Secure Data Sharing—0
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition—0
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence—0
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models—0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios—0
Learning Better Intent Representations for Financial Open Intent Classification—0
Towards Unifying Reference Expression Generation and ComprehensionCode0
Characterizing Verbatim Short-Term Memory in Neural Language ModelsCode0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding TasksCode0
A BERT-based Deep Learning Approach for Reputation Analysis in Social Media—0
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification—0
Do Language Models Understand Measurements?—0
Exploring the Value of Pre-trained Language Models for Clinical Named Entity RecognitionCode0
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data AugmentationCode0
Understanding Domain Learning in Language Models Through Subpopulation AnalysisCode0
LMPriors: Pre-Trained Language Models as Task-Specific Priors—0
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding—0
P^3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
Dissecting Deep Metric Learning Losses for Image-Text RetrievalCode0
A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT—0
Graphemic Normalization of the Perso-Arabic ScriptCode0
Is Encoder-Decoder Redundant for Neural Machine Translation?—0
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?Code0
Z-LaVI: Zero-Shot Language Solver Fueled by Visual ImaginationCode0
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation—0
LittleBird: Efficient Faster & Longer Transformer for Question Answering—0
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling—0
Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic AmbiguitiesCode0
Transcending Scaling Laws with 0.1% Extra Compute—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-LearningCode0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—0
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages—0
Language Detoxification with Attribute-Discriminative Latent SpaceCode0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation—0
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias BenchmarksCode0
Systematicity in GPT-3's Interpretation of Novel English Noun Compounds—0
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared TaskCode0
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared TaskCode0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
Continuous Pseudo-Labeling from the Start—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