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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1725117300 of 17610 papers

TitleStatusHype
Explanation Regeneration via Information BottleneckCode0
Item-side Fairness of Large Language Model-based Recommendation SystemCode0
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language ModelsCode0
Generative Visual Instruction TuningCode0
Generator-Assistant Stepwise Rollback Framework for Large Language Model AgentCode0
BERTtime Stories: Investigating the Role of Synthetic Story Data in Language pre-trainingCode0
Explicit Sparse Transformer: Concentrated Attention Through Explicit SelectionCode0
Contextual Grounding of Natural Language Entities in ImagesCode0
Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct FeaturesCode0
Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple LevelsCode0
Contextual Emotion Recognition Using Transformer-Based ModelsCode0
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated TextCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media TextsCode0
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model EvaluationCode0
Improving language models by retrieving from trillions of tokensCode0
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model PerformanceCode0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
BERTnesia: Investigating the capture and forgetting of knowledge in BERTCode0
A large language model-assisted education tool to provide feedback on open-ended responsesCode0
AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and CareCode0
Contextual Augmentation: Data Augmentation by Words with Paradigmatic RelationsCode0
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detectionCode0
Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun ResolutionCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
A Neural Model of Adaptation in ReadingCode0
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance ApproachCode0
Investigating Transferability in Pretrained Language ModelsCode0
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
An Ensemble Approach to Acronym Extraction using TransformersCode0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
An End-to-End Neural Network for Polyphonic Piano Music TranscriptionCode0
Context Retrieval via Normalized Contextual Latent Interaction for Conversational AgentCode0
Context-Free Transductions with Neural StacksCode0
An End-to-End Model for Photo-Sharing Multi-modal Dialogue GenerationCode0
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease DiagnosisCode0
Geographic Adaptation of Pretrained Language ModelsCode0
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human AssessmentCode0
Context-Driven Interactive Query Simulations Based on Generative Large Language ModelsCode0
Exploring and Verbalizing Academic Ideas by Concept Co-occurrenceCode0
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language ModelCode0
HUBERT Untangles BERT to Improve Transfer across NLP TasksCode0
HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasksCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
An Empirical Study on Pre-trained Embeddings and Language Models for Bot DetectionCode0
BERTgrid: Contextualized Embedding for 2D Document Representation and UnderstandingCode0
hULMonA: The Universal Language Model in ArabicCode0
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial AttacksCode0
Improving Large Language Model Safety with Contrastive Representation LearningCode0
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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