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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9101–9150 of 17610 papers

TitleStatusHype
LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Finding Answers to Questions from Qur’an—0
LASER: Linear Compression in Wireless Distributed Optimization—0
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision—0
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation—0
LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning—0
LAST: Language Model Aware Speech Tokenization—0
Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement—0
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings—0
Latent Principle Discovery for Language Model Self-Improvement—0
LatentQA: Teaching LLMs to Decode Activations Into Natural Language—0
Latent Structure Models for Natural Language Processing—0
Latent-Variable Generative Models for Data-Efficient Text Classification—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Lattice-based lightly-supervised acoustic model training—0
Lattice Desegmentation for Statistical Machine Translation—0
Lattice Rescoring for Speech Recognition using Large Scale Distributed Language Models—0
Latvian National Corpora Collection – Korpuss.lv—0
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts—0
Lawma: The Power of Specialization for Legal Tasks—0
LayerCollapse: Adaptive compression of neural networks—0
Layer Flexible Adaptive Computational Time—0
Layer Flexible Adaptive Computation Time for Recurrent Neural Networks—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
Layer-wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning—0
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration—0
LayoutBERT: Masked Language Layout Model for Object Insertion—0
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents—0
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System—0
LBPE: Long-token-first Tokenization to Improve Large Language Models—0
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven Language Representation—0
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling—0
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving—0
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid—0
Lean-STaR: Learning to Interleave Thinking and Proving—0
LEAP: LLM-Generation of Egocentric Action Programs—0
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—0
Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum—0
Learning and Evaluating a Differentially Private Pre-trained Language Model—0
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers—0
Learning and Unlearning of Fabricated Knowledge in Language Models—0
Learning Architectures from an Extended Search Space for Language Modeling—0
Learning Articulated Motion Models from Visual and Lingual Signals—0
Learning Attentional Mixture of LoRAs for Language Model Continual Learning—0
Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation—0
Learning-based Composite Metrics for Improved Caption 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