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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 62516300 of 17610 papers

TitleStatusHype
Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared TaskCode0
SOUL: Towards Sentiment and Opinion Understanding of LanguageCode0
Low-Resource Language Modelling of South African LanguagesCode0
Sound Natural: Content Rephrasing in Dialog SystemsCode0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
TigerLLM -- A Family of Bangla Large Language ModelsCode0
Tight Clusters Make Specialized ExpertsCode0
Lightweight Relevance Grader in RAGCode0
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-TrainingCode0
Large Memory Layers with Product KeysCode0
Understanding Hidden Computations in Chain-of-Thought ReasoningCode0
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual RetrievalCode0
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech TranslationCode0
Learning to Locate Visual Answer in Video Corpus Using QuestionCode0
SPACE-IDEAS: A Dataset for Salient Information Detection in Space InnovationCode0
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge MemoryCode0
Bias Amplification in Language Model Evolution: An Iterated Learning PerspectiveCode0
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationCode0
Revealing Vision-Language Integration in the Brain with Multimodal NetworksCode0
Span Selection Pre-training for Question AnsweringCode0
Typos-aware Bottlenecked Pre-Training for Robust Dense RetrievalCode0
Likelihood as a Performance Gauge for Retrieval-Augmented GenerationCode0
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource LanguagesCode0
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context LearningCode0
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language ModelsCode0
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image CaptioningCode0
Time-Efficient Code Completion Model for the R Programming LanguageCode0
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple MetricCode0
Revealing the structure of language model capabilitiesCode0
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence GenerationCode0
Time Matters: Examine Temporal Effects on Biomedical Language ModelsCode0
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike WaysCode0
On the Structural Memory of LLM AgentsCode0
On the State of the Art of Evaluation in Neural Language ModelsCode0
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language ExplanationsCode0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Multi-aspect Knowledge Distillation with Large Language ModelCode0
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling lawCode0
Sensei: Self-Supervised Sensor Name SegmentationCode0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense DisambiguationCode0
MedRep: Medical Concept Representation for General Electronic Health Record Foundation ModelsCode0
Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-PlayingCode0
MedMobile: A mobile-sized language model with expert-level clinical capabilitiesCode0
Sparseout: Controlling Sparsity in Deep NetworksCode0
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue GenerationCode0
Parsing as Language ModelingCode0
Sparse Sinkhorn AttentionCode0
Negation Triplet Extraction with Syntactic Dependency and Semantic ConsistencyCode0
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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