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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1640116450 of 17610 papers

TitleStatusHype
CIE: Controlling Language Model Text Generations Using Continuous SignalsCode0
IIT (BHU) Varanasi at MSR-SRST 2018: A Language Model Based Approach for Natural Language GenerationCode0
A Surprisingly Effective Fix for Deep Latent Variable Modeling of TextCode0
ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source InformationCode0
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-rankerCode0
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for DepressionCode0
Discrete Auto-regressive Variational Attention Models for Text ModelingCode0
Chunk-based Nearest Neighbor Machine TranslationCode0
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language ExplanationsCode0
Discrete Autoencoders for Sequence ModelsCode0
A surprisal oracle for when every layer countsCode0
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic AssistanceCode0
Choice Fusion as Knowledge for Zero-Shot Dialogue State TrackingCode0
ChipSong: A Controllable Lyric Generation System for Chinese Popular SongCode0
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender SystemsCode0
Chemical Language Model Linker: blending text and molecules with modular adaptersCode0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
Discovering Knowledge-Critical Subnetworks in Pretrained Language ModelsCode0
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4Code0
ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Low-Perplexity Toxic PromptsCode0
Improving Transformer Models by Reordering their SublayersCode0
Guiding In-Context Learning of LLMs through Quality Estimation for Machine TranslationCode0
ChatVis: Automating Scientific Visualization with a Large Language ModelCode0
Edisum: Summarizing and Explaining Wikipedia Edits at ScaleCode0
Discovering Spoofing Attempts on Language Model WatermarksCode0
A Multilingual, Culture-First Approach to Addressing Misgendering in LLM ApplicationsCode0
A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extractionCode0
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated ResponsesCode0
Discourse structure interacts with reference but not syntax in neural language modelsCode0
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to GraphsCode0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
Personalized Abstractive Summarization by Tri-agent Generation PipelineCode0
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation ExtractionCode0
DIS-CO: Discovering Copyrighted Content in VLMs Training DataCode0
ChatGPT in the context of precision agriculture data analyticsCode0
ChatGPT-guided Semantics for Zero-shot LearningCode0
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record GenerationCode0
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even PerformanceCode0
Direct Output Connection for a High-Rank Language ModelCode0
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the EdgeCode0
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error CorrectionCode0
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt DistillationCode0
Directed Beam Search: Plug-and-Play Lexically Constrained Language GenerationCode0
Fine-tuning the ESM2 protein language model to understand the functional impact of missense variantsCode0
Agglomerative AttentionCode0
Effective Estimation of Deep Generative Language ModelsCode0
CHARTOM: A Visual Theory-of-Mind Benchmark for Multimodal Large Language ModelsCode0
ChartFormer: A Large Vision Language Model for Converting Chart Images into Tactile Accessible SVGsCode0
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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