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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 91019150 of 17610 papers

TitleStatusHype
使用概念資訊於中文大詞彙連續語音辨識之研究 (Exploring Concept Information for Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]0
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls0
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model0
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models0
Exploring Disparate Language Model Combination Strategies for Mandarin-English Code-Switching ASR0
Exploring Domain Robust Lightweight Reward Models based on Router Mechanism0
Exploring Efficient Foundational Multi-modal Models for Video Summarization0
Exploring Extreme Quantization in Spiking Language Models0
Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics0
Exploring Forgetting in Large Language Model Pre-Training0
Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework0
Exploring Hyper-Parameter Optimization for Neural Machine Translation on GPU Architectures0
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text0
Exploring Interactive Semantic Alignment for Efficient HOI Detection with Vision-language Model0
Exploring intra-task relations to improve meta-learning algorithms0
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification0
Exploring Looping Effects in RNN-based Architectures0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning0
Exploring Methods for the Automatic Detection of Errors in Manual Transcription0
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection0
Exploring Multitask Learning for Low-Resource AbstractiveSummarization0
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS0
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.00
Exploring Neural Transducers for End-to-End Speech Recognition0
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications0
Exploring Options for Fast Domain Adaptation of Dependency Parsers0
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition0
Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models0
Exploring Prompt Engineering: A Systematic Review with SWOT Analysis0
Exploring prompts to elicit memorization in masked language model-based named entity recognition0
Exploring Properties of Intralingual and Interlingual Association Measures Visually0
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching0
Exploring RNN-Transducer for Chinese Speech Recognition0
Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes0
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model0
Exploring Scaling Laws for Local SGD in Large Language Model Training0
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models0
Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents0
Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation0
Exploring Software Naturalness through Neural Language Models0
Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction0
Exploring Spatial Schema Intuitions in Large Language and Vision Models0
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing0
Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model0
Exploring the Benefits of Tokenization of Discrete Acoustic Units0
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors0
Exploring the Effect of Dialect Mismatched Language Models in Telugu Automatic Speech Recognition0
Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified