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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5101–5150 of 17610 papers

TitleStatusHype
Disambiguating Symbolic Expressions in Informal Documents—0
Disaster Tweets Classification using BERT-Based Language Model—0
Discourse-Aware Soft Prompting for Text Generation—0
Discourse-Aware Prompt Design for Text Generation—0
Discovering Factions in the Computational Linguistics Community—0
Discovering Financial Hypernyms by Prompting Masked Language Models—0
Discovering Significant Topics from Legal Decisions with Selective Inference—0
Discovering Syntactic Interaction Clues for Human-Object Interaction Detection—0
Discovering Useful Sentence Representations from Large Pretrained Language Models—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Discrete Diffusion Language Model for Long Text Summarization—0
Discrete Modeling via Boundary Conditional Diffusion Processes—0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing—0
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding—0
Discrete Variational Attention Models for Language Generation—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
Discriminating between Mandarin Chinese and Swiss-German varieties using adaptive language models—0
Discriminating Non-Native English with 350 Words—0
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Discriminative Segmental Cascades for Feature-Rich Phone Recognition—0
Discriminative training of RNNLMs with the average word error criterion—0
Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions—0
Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions—0
Disentangled Prompt Representation for Domain Generalization—0
Disentangling Homophemes in Lip Reading using Perplexity Analysis—0
Disentangling Knowledge Representations for Large Language Model Editing—0
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning—0
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model—0
Disfluency Detection Using Multi-step Stacked Learning—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion—0
Distant-supervised Language Model for Detecting Emotional Upsurge on Twitter—0
Distill and Replay for Continual Language Learning—0
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach—0
Distillation Strategies for Discriminative Speech Recognition Rescoring—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs—0
Distilling Event Sequence Knowledge From Large Language Models—0
Distilling Knowledge from Pre-trained Language Models via Text Smoothing—0
Distilling Relation Embeddings from Pretrained Language Models—0
Distilling Relation Embeddings from Pre-trained Language Models—0
Distilling the Knowledge of BERT for CTC-based ASR—0
Distilling Vision-Language Models on Millions of Videos—0
DistillSpec: Improving Speculative Decoding via Knowledge Distillation—0
Distil-xLSTM: Learning Attention Mechanisms through Recurrent Structures—0
Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning—0
Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions—0
Distortion Model Considering Rich Context for Statistical Machine Translation—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