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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 63516400 of 17610 papers

TitleStatusHype
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence ModelingCode0
Patterns versus Characters in Subword-aware Neural Language ModelingCode0
MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language ModelCode0
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in JapaneseCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
Juman++: A Morphological Analysis Toolkit for Scriptio ContinuaCode0
Reasoning-Grounded Natural Language Explanations for Language ModelsCode0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
PAYADOR: A Minimalist Approach to Grounding Language Models on Structured Data for Interactive Storytelling and Role-playing GamesCode0
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language ModelsCode0
Pay Attention when RequiredCode0
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of ExemplarsCode0
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and VotingCode0
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language ModelCode0
KG-BERT: BERT for Knowledge Graph CompletionCode0
Learning Dynamic Author Representations with Temporal Language ModelsCode0
On the Proper Treatment of Tokenization in PsycholinguisticsCode0
Leveraging Training Data in Few-Shot Prompting for Numerical ReasoningCode0
Transformer based neural networks for emotion recognition in conversationsCode0
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-TuningCode0
PclGPT: A Large Language Model for Patronizing and Condescending Language DetectionCode0
A general language model for peptide identificationCode0
Semiparametric Token-Sequence Co-SupervisionCode0
Non-Determinism of "Deterministic" LLM SettingsCode0
Language-Model Prior Overcomes Cold-Start ItemsCode0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document GenerationCode0
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model AdaptationCode0
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge GraphsCode0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Relational recurrent neural networksCode0
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language ModelCode0
Spherical Latent Spaces for Stable Variational AutoencodersCode0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
On the Limitations of Sociodemographic Adaptation with TransformersCode0
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS MetathesaurusCode0
Leveraging Protein Language Model Embeddings for Catalytic Turnover Prediction of Adenylate Kinase Orthologs in a Low-Data RegimeCode0
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsCode0
Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across LanguagesCode0
Semantic Specialization for Knowledge-based Word Sense DisambiguationCode0
Semantics or spelling? Probing contextual word embeddings with orthographic noiseCode0
LegiLM: A Fine-Tuned Legal Language Model for Data ComplianceCode0
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2Code0
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge AlignmentCode0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Semantics-aware BERT for Language UnderstandingCode0
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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