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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12401–12450 of 17610 papers

TitleStatusHype
A Few-shot Approach to Resume Information Extraction via PromptsCode0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
From Disfluency Detection to Intent Detection and Slot FillingCode0
Selective Token Generation for Few-shot Natural Language GenerationCode0
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning—0
Can Offline Reinforcement Learning Help Natural Language Understanding?—0
PTab: Using the Pre-trained Language Model for Modeling Tabular Data—0
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
Stateful Memory-Augmented Transformers for Efficient Dialogue ModelingCode0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation—0
Out of One, Many: Using Language Models to Simulate Human Samples—0
Revisiting Neural Scaling Laws in Language and Vision—0
Exploring Code Style Transfer with Neural Networks—0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models—0
Improving Language Model Prompting in Support of Semi-autonomous Task Learning—0
Open-Domain Dialog Evaluation using Follow-Ups LikelihoodCode0
A Complex Network based Graph Embedding Method for Link Prediction—0
Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset—0
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue—0
Multi-Granularity Prediction for Scene Text RecognitionCode0
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages—0
Pre-Training a Graph Recurrent Network for Language RepresentationCode0
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language ModelCode0
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot ApproachCode0
Blessing of Class Diversity in Pre-training—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
Distilling the Knowledge of BERT for CTC-based ASR—0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
Selective Text Augmentation with Word Roles for Low-Resource Text ClassificationCode0
Do Large Language Models know what humans know?Code0
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Neural Approaches to Multilingual Information Retrieval—0
Vision-Language Adaptive Mutual Decoder for OOV-STR—0
Prefix Embeddings for In-context Machine Translation—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Efficient Sparsely Activated Transformers—0
Continuous QA Learning with Structured Prompts—0
The Fellowship of the Authors: Disambiguating Names from Social Network Context—0
To Adapt or to Fine-tune: A Case Study on Abstractive SummarizationCode0
Efficient and Interpretable Neural Models for Entity Tracking—0
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems—0
Personal Attribute Prediction from ConversationsCode0
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
On Unsupervised Training of Link Grammar Based Language Models—0
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context—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