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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13901–13950 of 17610 papers

TitleStatusHype
A Targeted Assessment of Incremental Processing in Neural LanguageModels and HumansCode0
Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction—0
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues—0
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation—0
Extracting Weighted Automata for Approximate Minimization in Language Modelling—0
BERTnesia: Investigating the capture and forgetting of knowledge in BERTCode0
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings—0
Language Model Metrics and Procrustes Analysis for Improved Vector Transformation of NLP Embeddings—0
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition—0
nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?—0
The Limitations of Limited Context for Constituency Parsing—0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification—0
A Span Extraction Approach for Information Extraction on Visually-Rich Documents—0
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial AttacksCode0
Learning to Select: A Fully Attentive Approach for Novel Object Captioning—0
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
Lower Perplexity is Not Always Human-LikeCode0
Target-Aware Data Augmentation for Stance Detection—0
Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals—0
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World—0
Transferring Representations of Logical Connectives—0
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining—0
On Randomized Classification Layers and Their Implications in Natural Language Generation—0
SCRIPT: Self-Critic PreTraining of Transformers—0
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words ExtractionCode0
Learning and Evaluating a Differentially Private Pre-trained Language Model—0
Structural Realization with GGNNs—0
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling—0
System description for ProfNER - SMMH: Optimized finetuning of a pretrained transformer and word vectors—0
LongSumm 2021: Session based automatic summarization model for scientific document—0
Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data PredictionCode0
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language ModelsCode0
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection—0
FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models—0
Are we there yet? Exploring clinical domain knowledge of BERT models—0
Investigating variation in written forms of Nahuatl using character-based language modelsCode0
Ad Headline Generation using Self-Critical Masked Language Model—0
Explainable Multi-hop Verbal Reasoning Through Internal Monologue—0
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI—0
DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT—0
Identification de profil clinique du patient: Une approche de classification de séquences utilisant des modèles de langage français contextualisés (Identification of patient clinical profiles : A sequence classification approach using contextualised French language models )—0
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers—0
Emotion-Infused Models for Explainable Psychological Stress DetectionCode0
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks—0
Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning—0
Text Summarization with Latent Queries—0
Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services—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