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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6001–6050 of 17610 papers

TitleStatusHype
An Incremental Algorithm for Transition-based CCG Parsing—0
An Independence-promoting Loss for Music Generation with Language Models—0
An In-depth Survey of Large Language Model-based Artificial Intelligence Agents—0
An Infinite Hierarchical Bayesian Model of Phrasal Translation—0
An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care—0
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition—0
An Interactive Agent Foundation Model—0
An Introduction to Vision-Language Modeling—0
An Inquiry into Datacenter TCO for LLM Inference with FP8—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
An investigation of phone-based subword units for end-to-end speech recognition—0
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese—0
An Iterative Algorithm for Rescaled Hyperbolic Functions Regression—0
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation—0
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
An LDA-based Topic Selection Approach to Language Model Adaptation for Handwritten Text Recognition—0
An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue—0
An LLM-Driven Multi-Agent Debate System for Mendelian Diseases—0
An LLM-Guided Tutoring System for Social Skills Training—0
An LP-based hyperparameter optimization model for language modeling—0
An MT Error-Driven Discriminative Word Lexicon using Sentence Structure Features—0
ANNA: Enhanced Language Representation for Question Answering—0
ANNA: Enhanced Language Representation for Question Answering—0
ANNA”:" Enhanced Language Representation for Question Answering—0
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations—0
A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language—0
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
An Omni-Font Gurmukhi to Shahmukhi Transliteration System—0
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views—0
An Online Attention-based Model for Speech Recognition—0
Towards the Anonymization of the Language Modeling—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
A novel approach to measuring the scope of patent claims based on probabilities obtained from (large) language models—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
A Novel DeBERTa-based Model for Financial Question Answering Task—0
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units—0
A Novel Joint Framework for Multiple Chinese Events Extraction—0
A novel molecule generative model of VAE combined with Transformer for unseen structure generation—0
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition—0
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks—0
A Novel Way of Identifying Cyber Predators—0
An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods—0
An overview of open source Deep Learning-based libraries for Neuroscience—0
An Overview Of Temporal Commonsense Reasoning and Acquisition—0
An Overview on Language Models: Recent Developments and Outlook—0
ANPMI: Assessing the True Comprehension Capabilities of LLMs for Multiple Choice Questions—0
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs—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