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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9151–9200 of 17610 papers

TitleStatusHype
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models—0
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking—0
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models—0
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks—0
Exploring the Potential of AI-Generated Synthetic Datasets: A Case Study on Telematics Data with ChatGPT—0
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models—0
Exploring the traditional NMT model and Large Language Model for chat translation—0
Exploring the Usage of Chinese Pinyin in Pretraining—0
Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study—0
Exploring the Use of Attention within an Neural Machine Translation Decoder States to Translate Idioms—0
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study—0
Exploring the Value of Personalized Word Embeddings—0
How Additional Knowledge can Improve Natural Language Commonsense Question Answering?—0
使用詞向量表示與概念資訊於中文大詞彙連續語音辨識之語言模型調適(Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Exponential Reservoir Sampling for Streaming Language Models—0
Exposing Attention Glitches with Flip-Flop Language Modeling—0
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings—0
Exposing the Limits of Video-Text Models through Contrast Sets—0
Expressive Text-to-Speech using Style Tag—0
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations—0
Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement—0
Extended Parallel Corpus for Amharic-English Machine Translation—0
Extended Study on Using Pretrained Language Models and YiSi-1 for Machine Translation Evaluation—0
Extended Translation Models in Phrase-based Decoding—0
Extending Memory for Language Modelling—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)—0
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results—0
Extensible Embedding: A Flexible Multipler For LLM's Context Length—0
Extensible Prompts for Language Models on Zero-shot Language Style Customization—0
External Knowledge Augmented Polyphone Disambiguation Using Large Language Model—0
External Language Model Integration for Factorized Neural Transducers—0
Extracting Adverse Drug Events from Clinical Notes—0
Extracting and Inferring Personal Attributes from Dialogue—0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT—0
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures—0
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context—0
Extracting chemical food safety hazards from the scientific literature automatically using large language models—0
Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows—0
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking—0
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study—0
Extracting Semantic Process Information from the Natural Language in Event Logs—0
Extracting Semantics from Maintenance Records—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model—0
Extracting Spatiotemporal Data from Gradients with Large Language Models—0
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3—0
Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods—0
Extracting Weighted Automata for Approximate Minimization in Language Modelling—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