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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1730117350 of 17610 papers

TitleStatusHype
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answeringCode0
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model InferenceCode0
BERT-Assisted Semantic Annotation Correction for Emotion-Related QuestionsCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
BendVLM: Test-Time Debiasing of Vision-Language EmbeddingsCode0
Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language ModelsCode0
Human-Centered LLM-Agent User Interface: A Position PaperCode0
A Data Cartography based MixUp for Pre-trained Language ModelsCode0
Improving Lemmatization of Non-Standard Languages with Joint LearningCode0
Context-aware Captions from Context-agnostic SupervisionCode0
Benchmarking Pre-trained Language Models for Multilingual NER: TraSpaS at the BSNLP2021 Shared TaskCode0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Humane Speech Synthesis through Zero-Shot Emotion and Disfluency GenerationCode0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
Exploring Graph Representations of Logical Forms for Language ModelingCode0
HumanEval on Latest GPT Models -- 2024Code0
Improving Lexical Embeddings with Semantic KnowledgeCode0
Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based ApproachCode0
Improving Low Compute Language Modeling with In-Domain Embedding InitialisationCode0
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question AnsweringCode0
Exploring Language Model Generalization in Low-Resource Extractive QACode0
Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language ModelingCode0
Consistency of a Recurrent Language Model With Respect to Incomplete DecodingCode0
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal DocumentsCode0
Protecting Copyrighted Material with Unique Identifiers in Large Language Model TrainingCode0
Investigating variation in written forms of Nahuatl using character-based language modelsCode0
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese HokkienCode0
Benchmarking Misuse Mitigation Against Covert AdversariesCode0
Exploring Multilingual Text Data DistillationCode0
Human in the Loop Adaptive Optimization for Improved Time Series ForecastingCode0
Benchmarking Long-tail Generalization with Likelihood SplitsCode0
Exploring Multitask Learning for Low-Resource Abstractive SummarizationCode0
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4Code0
GIRT-Model: Automated Generation of Issue Report TemplatesCode0
Benchmarking Large Language Model Uncertainty for Prompt OptimizationCode0
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic ForgettingCode0
Human-in-the-loop Machine Translation with Large Language ModelCode0
Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep HealthCode0
Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language ModelingCode0
Human-in-the-Loop Synthetic Text Data Inspection with Provenance TrackingCode0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
Inferring the Reader: Guiding Automated Story Generation with Commonsense ReasoningCode0
Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report GenerationCode0
BatchPrompt: Accomplish more with lessCode0
Considering Likelihood in NLP Classification Explanations with Occlusion and Language ModelingCode0
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel CorpusCode0
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLPCode0
GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language ModelCode0
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic SimilarityCode0
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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