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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1480114850 of 17610 papers

TitleStatusHype
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language ModelingCode0
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
Negation Triplet Extraction with Syntactic Dependency and Semantic ConsistencyCode0
Text-Driven Neural Collaborative Filtering Model for Paper Source TracingCode0
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsCode0
Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across LanguagesCode0
Refining the Responses of LLMs by ThemselvesCode0
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific RewardsCode0
Representation Degeneration Problem in Training Natural Language Generation ModelsCode0
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response TheoryCode0
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot FlyCode0
Navigating Nuance: In Quest for Political TruthCode0
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-AwarenessCode0
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model ReasoningCode0
Representation Learning of Daily Movement Data Using Text EncodersCode0
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language ModelsCode0
Representation of linguistic form and function in recurrent neural networksCode0
Speaker attribution in German parliamentary debates with QLoRA-adapted large language modelsCode0
Text Generation Based on Generative Adversarial Nets with Latent VariableCode0
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning AttacksCode0
Looking for a Handsome Carpenter! Debiasing GPT-3 Job AdvertisementsCode0
Representing visual classification as a linear combination of wordsCode0
REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion DetectionCode0
Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"Code0
Natural Language Understanding with Distributed RepresentationCode0
Reproducing and Regularizing the SCRN ModelCode0
Reproducing NevIR: Negation in Neural Information RetrievalCode0
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of MindCode0
Recycled Attention: Efficient inference for long-context language modelsCode0
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text RecognitionCode0
Investigating Recurrent Transformers with Dynamic HaltCode0
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?Code0
Deductive Additivity for Planning of Natural Language ProofsCode0
Long Short-Term Memory-Networks for Machine ReadingCode0
Text-in-Context: Token-Level Error Detection for Table-to-Text GenerationCode0
Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling TaskCode0
Length Optimization in Conformal PredictionCode0
Specify and Edit: Overcoming Ambiguity in Text-Based Image EditingCode0
Native Design Bias: Studying the Impact of English Nativeness on Language Model PerformanceCode0
Specious Sites: Tracking the Spread and Sway of Spurious News Stories at ScaleCode0
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language SpecificationsCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language ModelCode0
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language ModelsCode0
Reshaping Free-Text Radiology Notes Into Structured Reports With Generative TransformersCode0
Recurrent Neural Networks Hardware Implementation on FPGACode0
NarrowBERT: Accelerating Masked Language Model Pretraining and InferenceCode0
Recurrent Neural Network RegularizationCode0
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause AttachmentCode0
Recurrent Neural Network GrammarsCode0
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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