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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 57515800 of 17610 papers

TitleStatusHype
Reading Between the Lines: A dataset and a study on why some texts are tougher than othersCode0
Preference Optimization for Molecular Language ModelsCode0
Multi-Word Lexical SimplificationCode0
Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language ModelsCode0
Text-Driven Neural Collaborative Filtering Model for Paper Source TracingCode0
Reproducing NevIR: Negation in Neural Information RetrievalCode0
S2SNet: A Pretrained Neural Network for Superconductivity DiscoveryCode0
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating ApproachCode0
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model ReasoningCode0
Text Generation Based on Generative Adversarial Nets with Latent VariableCode0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience InterviewsCode0
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent SystemsCode0
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text GenerationCode0
Numeracy for Language Models: Evaluating and Improving their Ability to Predict NumbersCode0
Text-in-Context: Token-Level Error Detection for Table-to-Text GenerationCode0
Learning Natural Language Generation with Truncated Reinforcement LearningCode0
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language ModelsCode0
Towards the Generation of Musical Explanations with GPT-3Code0
Swap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context SwappingCode0
Reproducing and Regularizing the SCRN ModelCode0
TruthEval: A Dataset to Evaluate LLM Truthfulness and ReliabilityCode0
Let the Poem Hit the Rhythm: Using a Byte-Based Transformer for Beat-Aligned Poetry GenerationCode0
Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural ArchitecturesCode0
Preliminary Study on Incremental Learning for Large Language Model-based Recommender SystemsCode0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
Preparation and Usage of Xhosa Lexicographical Data for a Multilingual, Federated EnvironmentCode0
Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corporaCode0
Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word EmbeddingsCode0
RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News TextsCode0
Monolingual and Multilingual Reduction of Gender Bias in Contextualized RepresentationsCode0
Text Retrieval with Multi-Stage Re-Ranking ModelsCode0
Text Revision by On-the-Fly Representation OptimizationCode0
Preserving Generalization of Language models in Few-shot Continual Relation ExtractionCode0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Language-Enhanced Representation Learning for Single-Cell TranscriptomicsCode0
Monotonic Paraphrasing Improves Generalization of Language Model PromptingCode0
Locally Differentially Private Document Generation Using Zero Shot PromptingCode0
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language ModelsCode0
Quantifying Semantic Emergence in Language ModelsCode0
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language ModelsCode0
Towards understanding evolution of science through language model seriesCode0
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language ModelsCode0
Towards Understanding of Medical Randomized Controlled Trials by Conclusion GenerationCode0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Learning Parametric Distributions from Samples and PreferencesCode0
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional PriorCode0
ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word PredictionCode0
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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