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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10051–10075 of 17610 papers

TitleStatusHype
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval—0
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens—0
Model Stealing for Any Low-Rank Language Model—0
ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation—0
Modular Hybrid Autoregressive Transducer—0
Modular Networks: Learning to Decompose Neural Computation—0
Modulating Language Model Experiences through Frictions—0
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
MoIN: Mixture of Introvert Experts to Upcycle an LLM—0
MojoBench: Language Modeling and Benchmarks for Mojo—0
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model—0
Molly: Making Large Language Model Agents Solve Python Problem More Logically—0
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts—0
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline—0
Mondrian: Prompt Abstraction Attack Against Large Language Models for Cheaper API Pricing—0
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training—0
Monolingual corpus creation and evaluation of truly low-resource languages from Peru—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Moonshine: Distilling Game Content Generators into Steerable Generative Models—0
MoPe: Model Perturbation-based Privacy Attacks on Language Models—0
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—0
MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories—0
Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity—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