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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 2951–3000 of 17610 papers

TitleStatusHype
Future Token Prediction -- Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction—0
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation—0
Generalizations across filler-gap dependencies in neural language modelsCode0
LEGO: Language Model Building Blocks—0
Scaling Diffusion Language Models via Adaptation from Autoregressive ModelsCode3
MojoBench: Language Modeling and Benchmarks for Mojo—0
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
LMLPA: Language Model Linguistic Personality Assessment—0
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent CollaborationCode1
Lightweight Neural App Control—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
Scalable Influence and Fact Tracing for Large Language Model PretrainingCode1
Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement LearningCode0
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model AgentCode0
PAPILLON: Privacy Preservation from Internet-based and Local Language Model EnsemblesCode2
SaVe-TAG: Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs—0
Continuous Speech Tokenizer in Text To SpeechCode0
Automated Spinal MRI Labelling from Reports Using a Large Language ModelCode1
DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR—0
Remote Timing Attacks on Efficient Language Model Inference—0
Non-myopic Generation of Language Models for Reasoning and PlanningCode1
Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward PassesCode1
Self-calibration for Language Model Quantization and Pruning—0
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language ModelsCode0
Chatting with Bots: AI, Speech Acts, and the Edge of Assertion—0
GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks—0
Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment—0
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization—0
Improving Pinterest Search Relevance Using Large Language Models—0
PLDR-LLM: Large Language Model from Power Law Decoder RepresentationsCode0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
Exploring Forgetting in Large Language Model Pre-Training—0
Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User ModelingCode0
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language ModelingCode0
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning—0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity—0
KatzBot: Revolutionizing Academic Chatbot for Enhanced CommunicationCode0
No more hard prompts: SoftSRV prompting for synthetic data generation—0
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model—0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
Language Models are Symbolic Learners in Arithmetic—0
ComPO: Community Preferences for Language Model Personalization—0
SeisLM: a Foundation Model for Seismic WaveformsCode1
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin EvaluationCode0
Tokenization as Finite-State Transduction—0
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and StyleCode2
Generalized Probabilistic Attention Mechanism in Transformers—0
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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