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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 26012650 of 17610 papers

TitleStatusHype
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific ApplicationsCode1
GPT-too: A language-model-first approach for AMR-to-text generationCode1
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?Code1
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic RulesCode1
A Frustratingly Simple Decoding Method for Neural Text GenerationCode1
Enhancing Perception of Key Changes in Remote Sensing Image Change CaptioningCode1
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment GenerationCode1
Epidemic Modeling with Generative AgentsCode1
Enhancing Indic Handwritten Text Recognition Using Global Semantic InformationCode1
GraphLLM: Boosting Graph Reasoning Ability of Large Language ModelCode1
XMoE: Sparse Models with Fine-grained and Adaptive Expert SelectionCode1
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language ModelCode1
GraphXForm: Graph transformer for computer-aided molecular designCode1
Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series ForecastingCode1
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse GradientsCode1
Enhancing Dialogue Generation via Dynamic Graph Knowledge AggregationCode1
GREEK-BERT: The Greeks visiting Sesame StreetCode1
Enhancing Domain Adaptation through Prompt Gradient AlignmentCode1
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed GraphsCode1
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-GenerationCode1
ChemLLM: A Chemical Large Language ModelCode1
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
Grounding Language Models for Visual Entity RecognitionCode1
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics GraphCode1
Enhancing Conversational Search: Large Language Model-Aided Informative Query RewritingCode1
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
GypSum: Learning Hybrid Representations for Code SummarizationCode1
Bot or Human? Detecting ChatGPT Imposters with A Single QuestionCode1
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
Hallucinations in Large Multilingual Translation ModelsCode1
End-to-End Automatic Speech Recognition for GujaratiCode1
CharBERT: Character-aware Pre-trained Language ModelCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
Arabisc: Context-Sensitive Neural Spelling CheckerCode1
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue SystemsCode1
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry WritingCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-trainingCode1
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error CorrectionCode1
Endowing Protein Language Models with Structural KnowledgeCode1
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingCode1
Content-Based Collaborative Generation for Recommender SystemsCode1
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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