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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11601–11650 of 17610 papers

TitleStatusHype
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model—0
Analyzing the Performance of ChatGPT in Cardiology and Vascular Pathologies—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
OPI at SemEval 2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis—0
Stochastic Code Generation—0
The Self-Perception and Political Biases of ChatGPT—0
Mapping of attention mechanisms to a generalized Potts model—0
The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China—0
Learning Personalized Decision Support Policies—0
Priors for symbolic regressionCode0
A Reference Architecture for Designing Foundation Model based Systems—0
ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review—0
A-CAP: Anticipation Captioning with Commonsense Knowledge—0
ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning—0
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)—0
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection—0
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation—0
Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature—0
Measuring Gender Bias in West Slavic Language Models—0
Semantic Feature Verification in FLAN-T5—0
Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students—0
r-softmax: Generalized Softmax with Controllable Sparsity RateCode0
Training Large Language Models Efficiently with Sparsity and Dataflow—0
Teaching Large Language Models to Self-DebugCode0
Automated Reading Passage Generation with OpenAI's Large Language Model—0
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment—0
Similarity-Aware Multimodal Prompt Learning for Fake News Detection—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation—0
From Retrieval to Generation: Efficient and Effective Entity Set Expansion—0
Language-aware Multiple Datasets Detection Pretraining for DETRs—0
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling—0
Revolutionizing Single Cell Analysis: The Power of Large Language Models for Cell Type Annotation—0
Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Models—0
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT—0
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks—0
What's in a Name? Beyond Class Indices for Image Recognition—0
Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems—0
Unsupervised Improvement of Factual Knowledge in Language ModelsCode0
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA—0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data—0
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation—0
Dialogue-Contextualized Re-ranking for Medical History-Taking—0
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study—0
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document GenerationCode0
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department—0
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue ModelCode0
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System—0
A Measurement-Based Quantum-Like Language Model for Text Matching—0
Network Visualization of ChatGPT Research: a study based on term and keyword co-occurrence network analysis—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