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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9601–9650 of 17610 papers

TitleStatusHype
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa—0
WMT 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings—0
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
Source Attribution for Large Language Model-Generated Data—0
Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation—0
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling—0
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
Watching a Language Model Learning Chess—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors—0
Watermarking Language Models through Language Models—0
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark—0
WoNeF, an improved, expanded and evaluated automatic French translation of WordNet—0
Unsupervised Code-Switching for Multilingual Historical Document Transcription—0
Unleashing GPT on the Metaverse: Savior or Destroyer?—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition—0
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection—0
Wave Network: An Ultra-Small Language Model—0
WavLLM: Towards Robust and Adaptive Speech Large Language Model—0
WordAlchemy: A transformer-based Reverse Dictionary—0
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios—0
Weakly Supervised Dense Video Captioning—0
Undirected Machine Translation with Discriminative Reinforcement Learning—0
Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors—0
Weakly supervised information extraction from inscrutable handwritten document images—0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning—0
Word Alignment as Preference for Machine Translation—0
Unified Generative and Discriminative Training for Multi-modal Large Language Models—0
WatME: Towards Lossless Watermarking Through Lexical Redundancy—0
Wearable intelligent throat enables natural speech in stroke patients with dysarthria—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders—0
Word Alignment without NULL Words—0
Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT—0
Unified Multi-Criteria Chinese Word Segmentation with BERT—0
Weblio Pre-reordering Statistical Machine Translation System—0
WebMap -- Large Language Model-assisted Semantic Link Induction in the Web—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track—0
Weight decay induces low-rank attention layers—0
Weighted-Entropy-Based Quantization for Deep Neural Networks—0
WeightedKV: Attention Scores Weighted Key-Value Cache Merging for Large Language Models—0
Weighted Sampling for Masked Language Modeling—0
Weight Prediction Boosts the Convergence of AdamW—0
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models—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