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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9201–9250 of 17610 papers

TitleStatusHype
Extracting Weighted Language Lexicons from Wikipedia—0
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation—0
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing—0
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News—0
Extractive Summarisation Based on Keyword Profile and Language Model—0
Extractive Summary as Discrete Latent Variables—0
Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback—0
Extrapolating Multilingual Understanding Models as Multilingual Generators—0
Extremely Small BERT Models from Mixed-Vocabulary Training—0
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes—0
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation—0
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations—0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction—0
FAC^2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition—0
FaceInsight: A Multimodal Large Language Model for Face Perception—0
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection—0
Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments—0
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation—0
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models—0
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering—0
Using Large Language Model for End-to-End Chinese ASR and NER—0
Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students—0
Unnatural language processing: How do language models handle machine-generated prompts?—0
Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine—0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets—0
Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications—0
Using Large Language Models to Provide Explanatory Feedback to Human Tutors—0
Unpacking Large Language Models with Conceptual Consistency—0
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies—0
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media—0
Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers—0
Using LLMs to Model the Beliefs and Preferences of Targeted Populations—0
Using Morphological Knowledge in Open-Vocabulary Neural Language Models—0
Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020—0
Using PPM for Health Related Text Detection—0
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training—0
Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions—0
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style—0
Using Related Languages to Enhance Statistical Language Models—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
Using SMT for OCR Error Correction of Historical Texts—0
Using Social Media For Bitcoin Day Trading Behavior Prediction—0
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation—0
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation—0
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian—0
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation—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