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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 33013350 of 17610 papers

TitleStatusHype
Aligning LLM Agents by Learning Latent Preference from User EditsCode1
Reducing Transformer Depth on Demand with Structured DropoutCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online ContentCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
Beyond Size: How Gradients Shape Pruning Decisions in Large Language ModelsCode1
Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video UnderstandingCode1
Composable Text Controls in Latent Space with ODEsCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Composed Image Retrieval for Training-Free Domain ConversionCode1
Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model AlignmentCode1
Legilimens: Practical and Unified Content Moderation for Large Language Model ServicesCode1
Composing Parameter-Efficient Modules with Arithmetic OperationsCode1
Learning Vector-Quantized Item Representation for Transferable Sequential RecommendersCode1
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text PretrainingCode1
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target AtomsCode1
Compositional Chain-of-Thought Prompting for Large Multimodal ModelsCode1
Compositional Demographic Word EmbeddingsCode1
Learning To Retrieve Prompts for In-Context LearningCode1
CAREER: A Foundation Model for Labor Sequence DataCode1
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
Learning to Generate Grounded Visual Captions without Localization SupervisionCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference OptimizationCode1
Adversarial Training for Aspect-Based Sentiment Analysis with BERTCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Composition based oxidation state prediction of materials using deep learningCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Compressed Context Memory For Online Language Model InteractionCode1
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language ModelCode1
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-PrefixesCode1
Identifying the Risks of LM Agents with an LM-Emulated SandboxCode1
N-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language ModelCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
Resonance RoPE: Improving Context Length Generalization of Large Language ModelsCode1
If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept DescriptionsCode1
IFSeg: Image-free Semantic Segmentation via Vision-Language ModelCode1
CDLM: Cross-Document Language ModelingCode1
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term ConversationsCode1
Image Hijacks: Adversarial Images can Control Generative Models at RuntimeCode1
CodeQueries: A Dataset of Semantic Queries over CodeCode1
Learning to Attribute with AttentionCode1
Learning to engineer protein flexibilityCode1
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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