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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9851–9900 of 17610 papers

TitleStatusHype
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model—0
Memory Augmented Large Language Models are Computationally Universal—0
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition—0
Memory Augmented Neural Networks for Natural Language Processing—0
Memory-Augmented Recurrent Networks for Dialogue Coherence—0
Memory Backdoor Attacks on Neural Networks—0
Memory-based Parameter Adaptation—0
Memory, Consciousness and Large Language Model—0
Memory Efficient Adaptive Optimization—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference—0
Memory Representation in Transformer—0
Mental Disorder Classification via Temporal Representation of Text—0
MePT: Multi-Representation Guided Prompt Tuning for Vision-Language Model—0
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents—0
Merged bilingual trees based on Universal Dependencies in Machine Translation—0
Merino: Entropy-driven Design for Generative Language Models on IoT Devices—0
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification—0
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding—0
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training—0
Mesh-TensorFlow: Deep Learning for Supercomputers—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
Meta-aware Learning in text-to-SQL Large Language Model—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
Metadata Might Make Language Models Better—0
Meta-Designing Quantum Experiments with Language Models—0
Meta-Embeddings Based On Self-Attention—0
MetaICL: Learning to Learn In Context—0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization—0
Meta-Learning a Dynamical Language Model—0
Meta-Learning Fast Weight Language Models—0
Meta-Learning for Few-Shot Named Entity Recognition—0
Meta-learning via Language Model In-context Tuning—0
METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities—0
MetaRM: Shifted Distributions Alignment via Meta-Learning—0
Meta Semantic Template for Evaluation of Large Language Models—0
metaTextGrad: Automatically optimizing language model optimizers—0
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning—0
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models—0
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces—0
Methods to Estimate Large Language Model Confidence—0
MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model—0
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling—0
mGeNTE: A Multilingual Resource for Gender-Neutral Language and Translation—0
MGH Radiology Llama: A Llama 3 70B Model for Radiology—0
MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering—0
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries—0
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling—0
MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit—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