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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11801–11850 of 17610 papers

TitleStatusHype
FOSI: Hybrid First and Second Order OptimizationCode0
What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysis—0
Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education—0
The Capacity for Moral Self-Correction in Large Language Models—0
Augmented Language Models: a Survey—0
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition SystemsCode0
AI Chat Assistants can Improve Conversations about Divisive Topics—0
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction—0
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models—0
Modeling Complex Event Scenarios via Simple Entity-focused QuestionsCode0
Language Model Analysis for Ontology Subsumption Inference—0
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge—0
Symbolic Discovery of Optimization AlgorithmsCode0
Towards Agile Text Classifiers for Everyone—0
Predicting Class Distribution Shift for Reliable Domain Adaptive Object DetectionCode0
Diminished Diversity-of-Thought in a Standard Large Language Model—0
Distinguishability Calibration to In-Context LearningCode0
SemanticAC: Semantics-Assisted Framework for Audio Classification—0
Semantic Importance-Aware Communications Using Pre-trained Language Models—0
A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3—0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Adversarial Transformer Language Models for Contextual Commonsense Inference—0
Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval—0
Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-TuningCode0
Global Constraints with Prompting for Zero-Shot Event Argument ClassificationCode0
ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design—0
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning—0
Toolformer: Language Models Can Teach Themselves to Use ToolsCode0
Training-free Lexical Backdoor Attacks on Language ModelsCode0
Prompting for Multimodal Hateful Meme Classification—0
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models—0
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply InteractionsCode0
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance—0
Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingCode0
Algorithmic Collective Action in Machine Learning—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
Capturing Topic Framing via Masked Language Modeling—0
Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender SystemsCode0
Techniques to Improve Neural Math Word Problem SolversCode0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
FineDeb: A Debiasing Framework for Language ModelsCode0
Controlling for Stereotypes in Multimodal Language Model Evaluation—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—0
Witscript: A System for Generating Improvised Jokes in a Conversation—0
Witscript 2: A System for Generating Improvised Jokes Without Wordplay—0
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition—0
Creating a Large Language Model of a Philosopher—0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Semantic Coherence Markers for the Early Diagnosis of the Alzheimer DiseaseCode0
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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