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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1670116750 of 17610 papers

TitleStatusHype
AlphaZip: Neural Network-Enhanced Lossless Text CompressionCode0
Helpful assistant or fruitful facilitator? Investigating how personas affect language model behaviorCode0
A Self-feedback Knowledge Elicitation Approach for Chemical Reaction PredictionsCode0
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social MediaCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMsCode0
EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human ActionsCode0
Debiasing Pre-Trained Language Models via Efficient Fine-TuningCode0
Implicit N-grams Induced by RecurrenceCode0
A segmental framework for fully-unsupervised large-vocabulary speech recognitionCode0
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?Code0
Can Large Language Models Learn Independent Causal Mechanisms?Code0
DATETIME: A new benchmark to measure LLM translation and reasoning capabilitiesCode0
DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data VisualizationCode0
Frequency Is What You Need: Word-frequency Masking Benefits Vision-Language Model Pre-trainingCode0
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story VideosCode0
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity AlignmentCode0
Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for EducationCode0
A second-order-like optimizer with adaptive gradient scaling for deep learningCode0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
A Few-shot Approach to Resume Information Extraction via PromptsCode0
Data Similarity is Not Enough to Explain Language Model PerformanceCode0
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag CompetitionCode0
Encoder-Agnostic Adaptation for Conditional Language GenerationCode0
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley ValuesCode0
Data Noising as Smoothing in Neural Network Language ModelsCode0
Can Language Models Be Specific? How?Code0
Encoding word order in complex embeddingsCode0
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural NetworksCode0
A Comparison of Centrality Measures for Graph-Based Keyphrase ExtractionCode0
Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and RoadsidesCode0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
DATA: Differentiable ArchiTecture ApproximationCode0
AlphaZero Neural Scaling and Zipf's Law: a Tale of Board Games and Power LawsCode0
From Captions to Visual Concepts and BackCode0
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern ArchitecturesCode0
End-to-End Attention-based Large Vocabulary Speech RecognitionCode0
DataChat: Prototyping a Conversational Agent for Dataset Search and VisualizationCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
A Comparison of Adaptation Techniques and Recurrent Neural Network ArchitecturesCode0
Artificial intelligence in government: Concepts, standards, and a unified frameworkCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
ArthModel: Enhance Arithmetic Skills to Large Language ModelCode0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language ModelsCode0
Arrows of Time for Large Language ModelsCode0
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language ModelsCode0
DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic SpeakersCode0
Importance Weighting Can Help Large Language Models Self-ImproveCode0
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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