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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1700117050 of 17610 papers

TitleStatusHype
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General IntelligenceCode0
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question AnsweringCode0
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object DetectionCode0
CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster InformaticsCode0
Intra-Layer Recurrence in Transformers for Language ModelingCode0
HORAE: A Domain-Agnostic Language for Automated Service RegulationCode0
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context RepresentationsCode0
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learningCode0
A Non-monotonic Self-terminating Language ModelCode0
Generalizations across filler-gap dependencies in neural language modelsCode0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
Crisis Domain Adaptation Using Sequence-to-sequence TransformersCode0
Bootstrapping Text Anonymization Models with Distant SupervisionCode0
Creative GANs for generating poems, lyrics, and metaphorsCode0
CRCL at SemEval-2024 Task 2: Simple prompt optimizationsCode0
Aligned Music Notation and Lyrics TranscriptionCode0
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language ModelCode0
Boosting Zero-Shot Human-Object Interaction Detection with Vision-Language TransferCode0
Estimating Large Language Model Capabilities without Labeled Test DataCode0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class ClassificationCode0
Boosting Large Language Models with Mask Fine-TuningCode0
Boosting Disfluency Detection with Large Language Model as Disfluency GeneratorCode0
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model AgentCode0
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelCode0
Generalizing and Hybridizing Count-based and Neural Language ModelsCode0
Crafting In-context Examples according to LMs' Parametric KnowledgeCode0
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction TuningCode0
How about Time? Probing a Multilingual Language Model for Temporal RelationsCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data AugmentationCode0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language ModelCode0
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning inLanguage using neuro-symbolic cloud computingCode0
General Point Model Pretraining with Autoencoding and AutoregressiveCode0
Investigating the Impact of Data Selection Strategies on Language Model PerformanceCode0
BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response GenerationCode0
AnnoDPO: Protein Functional Annotation Learning with Direct Preference OptimizationCode0
Blockwise Self-Attention for Long Document UnderstandingCode0
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin EvaluationCode0
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 PatientsCode0
Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization ProblemsCode0
AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length ControlCode0
Intrinsic evaluation of language models for code-switchingCode0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
COVID-19 Vaccine Misinformation in Middle Income CountriesCode0
How Decoding Strategies Affect the Verifiability of Generated TextCode0
Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language ModelsCode0
Block-wise Dynamic SparsenessCode0
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCode0
Is Multilingual BERT Fluent in Language Generation?Code0
Understanding and Robustifying Differentiable Architecture SearchCode0
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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