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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

TitleStatusHype
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Aspect Oriented Suggestion Extraction from Online Reviews—0
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta InformationCode1
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language ModelingCode0
Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings—0
SPTS: Single-Point Text SpottingCode1
Value Retrieval with Arbitrary Queries for Form-like DocumentsCode1
Few-shot Multi-hop Question Answering over Knowledge Base—0
Deciphering antibody affinity maturation with language models and weakly supervised learningCode1
Towards Interactive Language Modeling—0
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Epigenomic language models powered by Cerebras—0
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising—0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Large Language Models are not Models of Natural Language: they are Corpus Models—0
Surfer100: Generating Surveys From Web Resources, Wikipedia-style—0
Step-unrolled Denoising Autoencoders for Text GenerationCode1
Controlled Cue Generation for Play Scripts—0
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts—0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks—0
Discourse-Aware Soft Prompting for Text Generation—0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation—0
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach—0
MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based FinetuningCode1
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Prompting Visual-Language Models for Efficient Video UnderstandingCode1
MLP Architectures for Vision-and-Language Modeling: An Empirical StudyCode1
JABER and SABER: Junior and Senior Arabic BERt—0
Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents—0
Zero-Shot Recommendation as Language ModelingCode1
A study on native American English speech recognition by Indian listeners with varying word familiarity level—0
Improving language models by retrieving from trillions of tokensCode0
GKS: Graph-based Knowledge Selector for Task-oriented Dialog System—0
A deep language model to predict metabolic network equilibria—0
Automated Story Generation as Question-Answering—0
FleetX—0
Quantifying Adaptability in Pre-trained Language Models with 500 TasksCode1
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering—0
Keeping it Simple: Language Models can learn Complex Molecular DistributionsCode1
End-to-end Adaptive Distributed Training on PaddlePaddle—0
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction—0
DIBERT: Dependency Injected Bidirectional Encoder Representations from TransformersCode0
Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—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