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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12351–12400 of 17610 papers

TitleStatusHype
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder—0
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model—0
Universal Conditional Masked Language Pre-training for Neural Machine Translation—0
VayuBuddy: an LLM-Powered Chatbot to Democratize Air Quality Insights—0
Using Word Embeddings for Automatic Query Expansion—0
VCD: Knowledge Base Guided Visual Commonsense Discovery in Images—0
VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It—0
VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model—0
Using word embedding for bio-event extraction—0
Using Term Position Similarity and Language Modeling for Bilingual Document Alignment—0
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning—0
VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning—0
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation—0
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation—0
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation—0
Vector-space calculation of semantic surprisal for predicting word pronunciation duration—0
Vector Space Models for Scientific Document Summarization—0
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian—0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling—0
Verb Knowledge Injection for Multilingual Event Processing—0
You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks—0
Verifiable Format Control for Large Language Model Generations—0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models—0
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation—0
VeriGen: A Large Language Model for Verilog Code Generation—0
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation—0
Using Social Media For Bitcoin Day Trading Behavior Prediction—0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
VERTa: a Linguistically-motivated Metric at the WMT15 Metrics Task—0
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention—0
Using SMT for OCR Error Correction of Historical Texts—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
VIANA: Visual Interactive Annotation of Argumentation—0
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation—0
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets—0
Using Related Languages to Enhance Statistical Language Models—0
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style—0
ViDAS: Vision-based Danger Assessment and Scoring—0
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification—0
Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions—0
Using PPM for Health Related Text Detection—0
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model—0
Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction—0
Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020—0
Video Description: A Survey of Methods, Datasets and Evaluation Metrics—0
Video Emotion Open-vocabulary Recognition Based on Multimodal Large Language Model—0
Using Morphological Knowledge in Open-Vocabulary Neural Language Models—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—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