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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4151–4200 of 17610 papers

TitleStatusHype
ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems—0
CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech—0
ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition—0
Chinese Couplet Generation with Neural Network Structures—0
Chinese Grammatical Error Diagnosis System Based on Hybrid Model—0
Chinese Grammatical Error Diagnosis Using Single Word Embedding—0
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning—0
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks—0
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches—0
Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model—0
Chinese Preposition Selection for Grammatical Error Diagnosis—0
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training—0
Chinese Song Iambics Generation with Neural Attention-based Model—0
Chinese Spelling Check based on N-gram and String Matching Algorithm—0
Chinese Spelling Checker Based on Statistical Machine Translation—0
機器翻譯為本的中文拼字改錯系統 (Chinese Spelling Checker Based on Statistical Machine Translation)—0
Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013—0
Chinese Spelling Check System Based on N-gram Model—0
Chinese Spelling Error Detection and Correction Based on Language Model, Pronunciation, and Shape—0
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model—0
Chinese Word Ordering Errors Detection and Correction for Non-Native Chinese Language Learners—0
Chinese Word Segmentation with Heterogeneous Graph Neural Network—0
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context—0
CHISPA on the GO: A mobile Chinese-Spanish translation service for travellers in trouble—0
Chitrarth: Bridging Vision and Language for a Billion People—0
Chittron: An Automatic Bangla Image Captioning System—0
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task—0
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming—0
ChronoFact: Timeline-based Temporal Fact Verification—0
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono—0
Chronologically Consistent Large Language Models—0
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data—0
Chunk-Distilled Language Modeling—0
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge—0
ChuXin: 1.6B Technical Report—0
CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data—0
CIEMPIESS: A New Open-Sourced Mexican Spanish Radio Corpus—0
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training—0
CigTime: Corrective Instruction Generation Through Inverse Motion Editing—0
CIKMar: A Dual-Encoder Approach to Prompt-Based Reranking in Educational Dialogue Systems—0
CINO: A Chinese Minority Pre-trained Language Model—0
CINO: A Chinese Minority Pre-trained Language Model—0
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning—0
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning—0
Circling Back to Recurrent Models of Language—0
Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models—0
CityCraft: A Real Crafter for 3D City Generation—0
CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System—0
City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization—0
CityLoc: 6DoF Pose Distributional Localization for Text Descriptions in Large-Scale Scenes with Gaussian Representation—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