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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 90519100 of 17610 papers

TitleStatusHype
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration0
Explainable Clinical Decision Support from Text0
Explainable CTR Prediction via LLM Reasoning0
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis0
Explainable Moral Values: a neuro-symbolic approach to value classification0
Explainable Multi-hop Verbal Reasoning Through Internal Monologue0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning0
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency0
Explaining Agent Behavior with Large Language Models0
Explaining Chest X-ray Pathology Models using Textual Concepts0
Explaining Face Presentation Attack Detection Using Natural Language0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)0
Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure0
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning0
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization0
Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving0
Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning0
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas0
Explicit and Implicit Syntactic Features for Text Classification0
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models0
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle0
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference0
Exploiting Contextual Target Attributes for Target Sentiment Classification0
Exploiting Language Model for Efficient Linguistic Steganalysis0
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task0
Exploiting Language Models for Visual Recognition0
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems0
Exploiting Morphological, Grammatical, and Semantic Correlates for Improved Text Difficulty Assessment0
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes0
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning0
Exploiting the large-scale German Broadcast Corpus to boost the Fraunhofer IAIS Speech Recognition System0
Detached Error Feedback for Distributed SGD with Random Sparsification0
Exploration of Masked and Causal Language Modelling for Text Generation0
Exploration of the Impact of Maximum Entropy in Recurrent Neural Network Language Models for Code-Switching Speech0
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF0
Exploring Adaptor Grammars for Native Language Identification0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison0
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity0
Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development0
Exploring an LM to generate Prolog Predicates from Mathematics Questions0
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer0
Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems0
Exploring ChatGPT and its Impact on Society0
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis0
Exploring Chinese Humor Generation: A Study on Two-Part Allegorical Sayings0
Exploring Code Style Transfer with Neural Networks0
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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