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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 35013550 of 17610 papers

TitleStatusHype
Data Augmentation using Pre-trained Transformer ModelsCode1
Contextual information integration for stance detection via cross-attentionCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on GeneralizationCode1
Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3Code1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Nonparametric Decoding for Generative RetrievalCode1
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI AgentsCode1
Contextualized Perturbation for Textual Adversarial AttackCode1
DARTS: Differentiable Architecture SearchCode1
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documentsCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNACode1
Decoding Speculative DecodingCode1
Condenser: a Pre-training Architecture for Dense RetrievalCode1
LLM-in-the-loop: Leveraging Large Language Model for Thematic AnalysisCode1
LLMs Can Simulate Standardized Patients via Agent CoevolutionCode1
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling ResearchCode1
MatFormer: Nested Transformer for Elastic InferenceCode1
Contextual Representation Learning beyond Masked Language ModelingCode1
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingCode1
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-ExplanationsCode1
AutoDIR: Automatic All-in-One Image Restoration with Latent DiffusionCode1
IterVM: Iterative Vision Modeling Module for Scene Text RecognitionCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
CycleFormer : TSP Solver Based on Language ModelingCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
LLMCBench: Benchmarking Large Language Model Compression for Efficient DeploymentCode1
JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed MetadataCode1
Sparse is Enough in Fine-tuning Pre-trained Large Language ModelsCode1
LLMDet: A Third Party Large Language Models Generated Text Detection ToolCode1
Sparse Modular Activation for Efficient Sequence ModelingCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
LLMBind: A Unified Modality-Task Integration FrameworkCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
CTRL: A Conditional Transformer Language Model for Controllable GenerationCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language TechnologiesCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
C-STS: Conditional Semantic Textual SimilarityCode1
LLaVA-SpaceSGG: Visual Instruct Tuning for Open-vocabulary Scene Graph Generation with Enhanced Spatial RelationsCode1
RetGen: A Joint framework for Retrieval and Grounded Text Generation ModelingCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital TwinsCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
A Neural Algorithm of Artistic StyleCode1
LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language ModelsCode1
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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