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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10101–10150 of 17610 papers

TitleStatusHype
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing—0
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation—0
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis—0
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System—0
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool—0
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition—0
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP—0
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue—0
Parallel Corpus Augmentation using Masked Language Models—0
Parallel Corpus Filtering via Pre-trained Language Models—0
Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Parallelizing Linear Transformers with the Delta Rule over Sequence Length—0
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries—0
ParallelSpec: Parallel Drafter for Efficient Speculative Decoding—0
PARAMANU-AYN: Pretrain from scratch or Continual Pretraining of LLMs for Legal Domain Adaptation?—0
PARAMANU-GANITA: Language Model with Mathematical Capabilities—0
ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost—0
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic—0
Parameter-Efficient Detoxification with Contrastive Decoding—0
Parameter-Efficient Fine-Tuning With Adapters—0
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning—0
Parameter Efficient Multimodal Transformers for Video Representation Learning—0
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion—0
Parameter-Efficient Tuning Helps Language Model Alignment—0
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters—0
Parameterized Neural Network Language Models for Information Retrieval—0
Parameter Re-Initialization through Cyclical Batch Size Schedules—0
Paraphrasing Compound Nominalizations—0
Paraphrasing Is All You Need for Novel Object Captioning—0
Paraphrasing with Large Language Models—0
ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics—0
ParFDA for Instance Selection for Statistical Machine Translation—0
Parkinson's disease diagnostics using AI and natural language knowledge transfer—0
PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric Videos—0
Parser Accuracy in Quality Estimation of Machine Translation: A Tree Kernel Approach—0
Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation—0
Parsing with Context Embeddings—0
Partially Mutual Exclusive Softmax for Positive and Unlabeled data—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Part-of-Speech Tagger for Bodo Language using Deep Learning approach—0
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning—0
Passive and Pervasive Use of Bilingual Dictionary in Statistical Machine Translation—0
PASTA: Pretrained Action-State Transformer Agents—0
Patchwork: A Unified Framework for RAG Serving—0
PatentGPT: A Large Language Model for Intellectual Property—0
PathAlign: A vision-language model for whole slide images in histopathology—0
PaTH Attention: Position Encoding via Accumulating Householder Transformations—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