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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10451–10500 of 17610 papers

TitleStatusHype
Prevalence and prevention of large language model use in crowd work—0
Preventing Gradient Explosions in Gated Recurrent Units—0
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs—0
PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes—0
PRIMO: Progressive Induction for Multi-hop Open Rule Generation—0
Principled Gradient-based Markov Chain Monte Carlo for Text Generation—0
PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue—0
PRISM: A Design Framework for Open-Source Foundation Model Safety—0
PRISMe: A Novel LLM-Powered Tool for Interactive Privacy Policy Assessment—0
PRISM: Preference Refinement via Implicit Scene Modeling for 3D Vision-Language Preference-Based Reinforcement Learning—0
Natural Language Understanding with Privacy-Preserving BERT—0
MeanCache: User-Centric Semantic Caching for LLM Web Services—0
Privacy-Preserving Instructions for Aligning Large Language Models—0
Privacy-Preserving Language Model Inference with Instance Obfuscation—0
Privacy-Preserving Prompt Tuning for Large Language Model Services—0
Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation—0
Private Language Model Adaptation for Speech Recognition—0
PrivateLoRA For Efficient Privacy Preserving LLM—0
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning—0
Private Text Generation by Seeding Large Language Model Prompts—0
Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery—0
PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification—0
Prix-LM: Pretraining for Multilingual Knowledge Base Construction—0
Probabilistic Adaptation of Text-to-Video Models—0
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities—0
Probabilistic Modeling of Joint-context in Distributional Similarity—0
Probabilistic Modelling of Morphologically Rich Languages—0
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling—0
Probing BERT’s priors with serial reproduction chains—0
Probing Multi-modal Machine Translation with Pre-trained Language Model—0
Probing neural language models for understanding of words of estimative probability—0
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning—0
Probing phoneme, language and speaker information in unsupervised speech representations—0
Probing Representations Learned by Multimodal Recurrent and Transformer Models—0
Probing Statistical Representations For End-To-End ASR—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Probing the topology of the space of tokens with structured prompts—0
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension—0
Procedurally generating rules to adapt difficulty for narrative puzzle games—0
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT—0
ProcessBERT: Towards Equivalence Judgment of Variable Definitions among Multiple Engineering Documents—0
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets—0
Process Knowledge-infused Learning for Clinician-friendly Explanations—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
PRODIS - a speech database and a phoneme-based language model for the study of predictability effects in Polish—0
Product Review Summarization by Exploiting Phrase Properties—0
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text—0
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models—0
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction—0
Progress and Tradeoffs in Neural Language Models—0
Show:102550
← PrevPage 210 of 353Next →
All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified