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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12901–12950 of 17610 papers

TitleStatusHype
SM70: A Large Language Model for Medical Devices—0
Smaller Large Language Models Can Do Moral Self-Correction—0
Small Language Model as Data Prospector for Large Language Model—0
Small Language Model Can Self-correct—0
Small Language Models as Effective Guides for Large Language Models in Chinese Relation Extraction—0
Small Language Model Meets with Reinforced Vision Vocabulary—0
Small Language Models are Equation Reasoners—0
Small Languages, Big Models: A Study of Continual Training on Languages of Norway—0
Small Model and In-Domain Data Are All You Need—0
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare—0
Small-Scale Cross-Language Authorship Attribution on Social Media Comments—0
Small-to-Large Generalization: Data Influences Models Consistently Across Scale—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference—0
Smart Computer Aided Translation Environment - SCATE—0
Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection—0
SmartLLM: Smart Contract Auditing using Custom Generative AI—0
SmartWay: Enhanced Waypoint Prediction and Backtracking for Zero-Shot Vision-and-Language Navigation—0
Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating—0
SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision—0
SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration—0
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
Smoothed marginal distribution constraints for language modeling—0
Smoothing parameter estimation framework for IBM word alignment models—0
State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solving—0
SMR: State Memory Replay for Long Sequence Modeling—0
Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G—0
Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild—0
SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection—0
S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging—0
Long Context Compression with Activation Beacon—0
SOCIA: An End-to-End Agentic Framework for Automated Cyber-Physical-Social Simulator Generation—0
SocialBERT -- Transformers for Online SocialNetwork Language Modelling—0
Social Life Simulation for Non-Cognitive Skills Learning—0
Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions—0
SocialQuotes: Learning Contextual Roles of Social Media Quotes on the Web—0
Societal Impacts Research Requires Benchmarks for Creative Composition Tasks—0
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following—0
Socratic Reasoning Improves Positive Text Rewriting—0
SoftAdam: Unifying SGD and Adam for better stochastic gradient descent—0
Soft Best-of-n Sampling for Model Alignment—0
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition—0
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training—0
Softmax Bias Correction for Quantized Generative Models—0
Soft-prompt Tuning for Large Language Models to Evaluate Bias—0
Software Metadata Classification based on Generative Artificial Intelligence—0
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer—0
Solution for SMART-101 Challenge of CVPR Multi-modal Algorithmic Reasoning Task 2024—0
Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modeling—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