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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 85018550 of 17610 papers

TitleStatusHype
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs0
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning0
Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines0
Dynamic Terminology Integration Methods in Statistical Machine Translation0
Dynamic Topic Adaptation for Phrase-based MT0
Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes0
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling0
E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection0
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model0
EAGLE: Egocentric AGgregated Language-video Engine0
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization0
Early Improving Recurrent Elastic Highway Network0
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model0
Early Stage LM Integration Using Local and Global Log-Linear Combination0
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models0
EAZY: Eliminating Hallucinations in LVLMs by Zeroing out Hallucinatory Image Tokens0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce0
E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction0
EC^2: Emergent Communication for Embodied Control0
EC2: Emergent Communication for Embodied Control0
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction0
Echo: A Large Language Model with Temporal Episodic Memory0
Echo-Attention: Attend Once and Get N Attentions for Free0
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning0
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation0
ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing0
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter0
Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference0
edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts0
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness0
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior0
EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs0
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization0
Edinburgh's Machine Translation Systems for European Language Pairs0
Edinburgh's Phrase-based Machine Translation Systems for WMT-140
Edinburgh's Statistical Machine Translation Systems for WMT160
Edinburgh's Syntax-Based Systems at WMT 20150
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT0
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes0
Editing Arbitrary Propositions in LLMs without Subject Labels0
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?0
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts0
EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues0
EditLord: Learning Code Transformation Rules for Code Editing0
EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM0
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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