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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5551–5600 of 17610 papers

TitleStatusHype
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams—0
A Dataset and Benchmarks for Multimedia Social Analysis—0
A Dataset and Classifier for Recognizing Social Media English—0
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology—0
Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations—0
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size—0
Adding Recurrence to Pretrained Transformers—0
Additive Neural Networks for Statistical Machine Translation—0
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality—0
Addressing the Winograd Schema Challenge as a Sequence Ranking Task—0
AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
A*-Decoding: Token-Efficient Inference Scaling—0
A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?—0
A Deep Dive into Word Sense Disambiguation with LSTM—0
A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition—0
A Deep Generative Approach to Native Language Identification—0
A deep language model to predict metabolic network equilibria—0
A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism—0
A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition—0
Ad Headline Generation using Self-Critical Masked Language Model—0
A Difference-of-Convex Programming Approach With Parallel Branch-and-Bound For Sentence Compression Via A Hybrid Extractive Model—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
A Discriminative Entity-Aware Language Model for Virtual Assistants—0
A Discriminative Model for Query Spelling Correction with Latent Structural SVM—0
A Discriminative Training Procedure for Continuous Translation Models—0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements—0
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation—0
A Dot Product Attention Free Transformer—0
A Driver Advisory System Based on Large Language Model for High-speed Train—0
Ad Text Classification with Transformer-Based Natural Language Processing Methods—0
Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors—0
Advanced simulation paradigm of human behaviour unveils complex financial systemic projection—0
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis—0
Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA—0
Advancements in Reordering Models for Statistical Machine Translation—0
Advances in All-Neural Speech Recognition—0
Advances in apparent conceptual physics reasoning in GPT-4—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Advancing Acoustic-to-Word CTC Model—0
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units—0
Advancing Chinese biomedical text mining with community challenges—0
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies—0
Advancing Complex Medical Communication in Arabic with Sporo AraSum: Surpassing Existing Large Language Models—0
Advancing Connectionist Temporal Classification With Attention Modeling—0
Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application—0
Advancing Large Language Model Attribution through Self-Improving—0
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation—0
Show:102550
← PrevPage 112 of 353Next →
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