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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 41514200 of 17610 papers

TitleStatusHype
Enhancing SPARQL Generation by Triplet-order-sensitive Pre-trainingCode0
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition SystemsCode0
A Sentiment-annotated Dataset of English Causal ConnectivesCode0
Confidential Prompting: Protecting User Prompts from Cloud LLM ProvidersCode0
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document MatchingCode0
Coordinate-Aware Thermal Infrared Tracking Via Natural Language ModelingCode0
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-TuningCode0
Enhancing Tool Retrieval with Iterative Feedback from Large Language ModelsCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
AMuRD: Annotated Arabic-English Receipt Dataset for Key Information Extraction and ClassificationCode0
Enhancing Vision-Language Model Pre-training with Image-text Pair Pruning Based on Word FrequencyCode0
EnIGMA: Enhanced Interactive Generative Model Agent for CTF ChallengesCode0
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMsCode0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
Enriching BERT with Knowledge Graph Embeddings for Document ClassificationCode0
Enriching language models with graph-based context information to better understand textual dataCode0
Enriching Pre-trained Language Model with Entity Information for Relation ClassificationCode0
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text CorrespondenceCode0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD VariantsCode0
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation RecognitionCode0
ConQueR: Contextualized Query Reduction using Search LogsCode0
TAGPRIME: A Unified Framework for Relational Structure ExtractionCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data AugmentationCode0
Considering Likelihood in NLP Classification Explanations with Occlusion and Language ModelingCode0
Entailment Semantics Can Be Extracted from an Ideal Language ModelCode0
Enterprise Benchmarks for Large Language Model EvaluationCode0
A Simple Baseline for Predicting Events with Auto-Regressive Tabular TransformersCode0
Entities as Experts: Sparse Memory Access with Entity SupervisionCode0
Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model GeneralizationCode0
Entity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans DetectionCode0
Consistency of a Recurrent Language Model With Respect to Incomplete DecodingCode0
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation ExtractionCode0
Beyond Language: Learning Commonsense from Images for ReasoningCode0
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 SurprisalCode0
A Simple Cache Model for Image RecognitionCode0
Beyond Ontology in Dialogue State Tracking for Goal-Oriented ChatbotCode0
Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade DirectoriesCode0
Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment DomainCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language ModelingCode0
Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon SimulationCode0
Summarization-Based Document IDs for Generative Retrieval with Language ModelsCode0
ERASMO: Leveraging Large Language Models for Enhanced Clustering SegmentationCode0
ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation GeneralizationCode0
ERNIE-Doc: A Retrospective Long-Document Modeling TransformerCode0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Context-aware Captions from Context-agnostic SupervisionCode0
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced LanguagesCode0
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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