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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 82518300 of 17610 papers

TitleStatusHype
PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows0
A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing0
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language ModelCode0
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection0
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring0
A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extractionCode0
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles0
Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific LiteratureCode1
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
TRAMS: Training-free Memory Selection for Long-range Language ModelingCode1
Unnatural language processing: How do language models handle machine-generated prompts?0
Vision-Language Pseudo-Labels for Single-Positive Multi-Label LearningCode1
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models0
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity0
Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model0
DoGE: Domain Reweighting with Generalization Estimation0
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuningCode2
Irreducible Curriculum for Language Model Pretraining0
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific RewardsCode0
Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation0
Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True DistributionCode0
Chain-of-Factors Paper-Reviewer MatchingCode0
Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering0
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed GraphsCode1
GeoLM: Empowering Language Models for Geospatially Grounded Language UnderstandingCode1
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
SpecTr: Fast Speculative Decoding via Optimal Transport0
LLM-in-the-loop: Leveraging Large Language Model for Thematic AnalysisCode1
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions0
Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language ModelsCode0
QUDEVAL: The Evaluation of Questions Under Discussion Discourse ParsingCode0
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research0
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language UnderstandingCode1
Large Search Model: Redefining Search Stack in the Era of LLMs0
Understanding the Inner Workings of Language Models Through Representation Dissimilarity0
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument CompatibilitiesCode0
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation0
Improving Seq2Seq Grammatical Error Correction via Decoding InterventionsCode1
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic RulesCode0
PHD: Pixel-Based Language Modeling of Historical DocumentsCode0
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the WebCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data0
Customising General Large Language Models for Specialised Emotion Recognition Tasks0
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and BiasCode0
Text generation for dataset augmentation in security classification tasksCode1
Orthogonal Subspace Learning for Language Model Continual LearningCode1
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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