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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1420114250 of 17610 papers

TitleStatusHype
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction0
Independent language modeling architecture for end-to-end ASR0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models0
Indian Sign Language Recognition Using Mediapipe Holistic0
Indicatements that character language models learn English morpho-syntactic units and regularities0
`Indicatements' that character language models learn English morpho-syntactic units and regularities0
Individual corpora predict fast memory retrieval during reading0
IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces0
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition0
Indonesian Automatic Speech Recognition with XLSR-530
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies0
IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language0
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages0
Inducing Group Fairness in Prompt-Based Language Model Decisions0
Inducing Relational Knowledge from BERT0
Inducing Syntactic Trees from BERT Representations0
Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models0
Inductive Graph Embeddings through Locality Encodings0
Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept0
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models0
InfAlign: Inference-aware language model alignment0
Inference Compute-Optimal Video Vision Language Models0
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling0
Inference Scaling for Bridging Retrieval and Augmented Generation0
Inference Strategies for Machine Translation with Conditional Masking0
Inference-Time Language Model Alignment via Integrated Value Guidance0
Inferring Pluggable Types with Machine Learning0
InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU0
Influence of ASR and Language Model on Alzheimer's Disease Detection0
Influence of Solution Efficiency and Valence of Instruction on Additive and Subtractive Solution Strategies in Humans and GPT-40
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models0
InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents0
InfoBridge: Mutual Information estimation via Bridge Matching0
InfoPO: On Mutual Information Maximization for Large Language Model Alignment0
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding0
Infor-Coef: Information Bottleneck-based Dynamic Token Downsampling for Compact and Efficient language model0
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations0
Information Density as a Factor for Variation in the Embedding of Relative Clauses0
Information Extraction from Historical Well Records Using A Large Language Model0
Information Flow Control in Machine Learning through Modular Model Architecture0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining0
Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek0
Infrared and Visible Image Fusion with Hierarchical Human Perception0
Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF0
Infusing Future Information into Monotonic Attention Through Language Models0
In Generative AI we Trust: Can Chatbots Effectively Verify Political Information?0
Using an LLM to Help With Code Understanding0
Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR0
Injecting Text in Self-Supervised Speech Pretraining0
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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