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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11651–11700 of 17610 papers

TitleStatusHype
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
ReviewEval: An Evaluation Framework for AI-Generated Reviews—0
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning—0
Revising the METU-Sabanc Turkish Treebank: An Exercise in Surface-Syntactic Annotation of Agglutinative Languages—0
Revisited Large Language Model for Time Series Analysis through Modality Alignment—0
Revisit, Extend, and Enhance Hessian-Free Influence Functions—0
Revisiting Activation Regularization for Language RNNs—0
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training—0
Revisiting Bayes by Backprop—0
Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language—0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification—0
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models—0
Revisiting Neural Language Modelling with Syllables—0
Revisiting Neural Scaling Laws in Language and Vision—0
Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text Recognition—0
Revisiting Representation Degeneration Problem in Language Modeling—0
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning—0
Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation—0
Revisiting the Case for Explicit Syntactic Information in Language Models—0
Revisiting the Hierarchical Multiscale LSTM—0
Revisiting the Task of Scoring Open IE Relations—0
Revitalizing Saturated Benchmarks: A Weighted Metric Approach for Differentiating Large Language Model Performance—0
Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices—0
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information—0
Revolutionizing Single Cell Analysis: The Power of Large Language Models for Cell Type Annotation—0
Rewarding Chatbots for Real-World Engagement with Millions of Users—0
Reward Modeling for Mitigating Toxicity in Transformer-based Language Models—0
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting—0
ReXplain: Translating Radiology into Patient-Friendly Video Reports—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
Rhetorical relations for information retrieval—0
RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL—0
Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling—0
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence—0
Rich Morphology Generation Using Statistical Machine Translation—0
Right Place, Right Time! Dynamizing Topological Graphs for Embodied Navigation—0
RigoBERTa: A State-of-the-Art Language Model For Spanish—0
RINAS: Training with Dataset Shuffling Can Be General and Fast—0
RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data—0
Risks and NLP Design: A Case Study on Procedural Document QA—0
Risks of Cultural Erasure in Large Language Models—0
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems—0
RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models—0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs—0
RL with KL penalties is better viewed as Bayesian inference—0
R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge—0
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems—0
RNN-T For Latency Controlled ASR With Improved Beam Search—0
Show:102550
← PrevPage 234 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