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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 24512500 of 17610 papers

TitleStatusHype
A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language ProcessingCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUsCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?Code1
Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3Code1
Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMCode1
A Generative Approach for Script Event Prediction via Contrastive Fine-tuningCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language ModelingCode1
Jump to Conclusions: Short-Cutting Transformers With Linear TransformationsCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
C-STS: Conditional Semantic Textual SimilarityCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language ModelCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
A Generative Language Model for Few-shot Aspect-Based Sentiment AnalysisCode1
A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval GreekCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilityCode1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack FrameworkCode1
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from TextCode1
Counterfactual Token Generation in Large Language ModelsCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
CycleFormer : TSP Solver Based on Language ModelingCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language ModelsCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language ModelCode1
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documentsCode1
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language ModelsCode1
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNACode1
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
DARTS: Differentiable Architecture SearchCode1
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem UnderstandingCode1
Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility PredictionCode1
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User InstructionsCode1
cosFormer: Rethinking Softmax in AttentionCode1
InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofingCode1
Data Movement Is All You Need: A Case Study on Optimizing TransformersCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
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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