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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10451–10500 of 17610 papers

TitleStatusHype
The Capacity for Moral Self-Correction in Large Language Models—0
Modeling Complex Event Scenarios via Simple Entity-focused QuestionsCode0
AI Chat Assistants can Improve Conversations about Divisive Topics—0
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models—0
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction—0
Language Model Analysis for Ontology Subsumption Inference—0
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource DomainsCode1
Symbolic Discovery of Optimization AlgorithmsCode0
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge—0
Simple Hardware-Efficient Long Convolutions for Sequence ModelingCode2
Diminished Diversity-of-Thought in a Standard Large Language Model—0
An Empirical Evaluation of Using Large Language Models for Automated Unit Test GenerationCode2
Distinguishability Calibration to In-Context LearningCode0
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Predicting Class Distribution Shift for Reliable Domain Adaptive Object DetectionCode0
Towards Agile Text Classifiers for Everyone—0
Semantic Importance-Aware Communications Using Pre-trained Language Models—0
SemanticAC: Semantics-Assisted Framework for Audio Classification—0
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLCode2
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
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
Toolformer: Language Models Can Teach Themselves to Use ToolsCode0
ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design—0
Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-TuningCode0
Global Constraints with Prompting for Zero-Shot Event Argument ClassificationCode0
In-Context Learning with Many Demonstration ExamplesCode1
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning—0
Prompting for Multimodal Hateful Meme Classification—0
Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingCode0
Algorithmic Collective Action in Machine Learning—0
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance—0
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply InteractionsCode0
Training-free Lexical Backdoor Attacks on Language ModelsCode0
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models—0
Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender SystemsCode0
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model StudyCode1
UDApter -- Efficient Domain Adaptation Using AdaptersCode1
Capturing Topic Framing via Masked Language Modeling—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Techniques to Improve Neural Math Word Problem SolversCode0
FineDeb: A Debiasing Framework for Language ModelsCode0
Representation Deficiency in Masked Language ModelingCode1
Witscript 2: A System for Generating Improvised Jokes Without Wordplay—0
Witscript: A System for Generating Improvised Jokes in a Conversation—0
Bioformer: an efficient transformer language model for biomedical text miningCode1
Controlling for Stereotypes in Multimodal Language Model Evaluation—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