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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5601–5650 of 17610 papers

TitleStatusHype
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy—0
Advancing RNN Transducer Technology for Speech Recognition—0
Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning—0
Advantage Alignment Algorithms—0
Adverbs, Surprisingly—0
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis—0
Adversarial Contrastive Pre-training for Protein Sequences—0
Adversarial Examples for DNA Classification—0
Robustness to Modification with Shared Words in Paraphrase Identification—0
Adversarial Generation of Natural Language—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Adversarial Representation Learning for Text-to-Image Matching—0
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis—0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model—0
Adversarial Training of Word2Vec for Basket Completion—0
Adversarial Training with Contrastive Learning in NLP—0
Adversarial Transfer Learning for Punctuation Restoration—0
Adversarial Transformer Language Models for Contextual Commonsense Inference—0
Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning—0
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness—0
A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices—0
以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In Chinese]—0
Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning—0
A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling—0
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies—0
A Fast, Performant, Secure Distributed Training Framework For Large Language Model—0
A federated large language model for long-term time series forecasting—0
Affect-LM: A Neural Language Model for Customizable Affective Text Generation—0
AffectON: Incorporating Affect Into Dialog Generation—0
Affordance Perception by a Knowledge-Guided Vision-Language Model with Efficient Error Correction—0
A Financial Service Chatbot based on Deep Bidirectional Transformers—0
A Fine-Grained Analysis of BERTScore—0
A Finite-State Approach to Phrase-Based Statistical Machine Translation—0
A First South African Corpus of Multilingual Code-switched Soap Opera Speech—0
A Flexible Approach to Automated RNN Architecture Generation—0
α-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models—0
A Foundational Multimodal Vision Language AI Assistant for Human Pathology—0
A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI—0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN—0
A framework for anomaly detection using language modeling, and its applications to finance—0
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts—0
A Framework for Decoding Event-Related Potentials from Text—0
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture—0
A Framework for Evaluating LLMs Under Task Indeterminacy—0
A Framework for Evaluating Vision-Language Model Safety: Building Trust in AI for Public Sector Applications—0
A Framework for Real-time Safeguarding the Text Generation of Large Language Model—0
The Responsible Development of Automated Student Feedback with Generative AI—0
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies—0
AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation—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