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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16951–17000 of 17610 papers

TitleStatusHype
CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain—0
Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker—0
CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
D3PO: Preference-Based Alignment of Discrete Diffusion Models—0
D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents—0
DACT-BERT: Increasing the efficiency and interpretability of BERT by using adaptive computation time.—0
DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP—0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase—0
Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic—0
DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition—0
DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition—0
DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction—0
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory—0
DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
DarkBERT: A Language Model for the Dark Side of the Internet—0
DARTS: Dialectal Arabic Transcription System—0
Data Augmentation for Biomedical Factoid Question Answering—0
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts—0
Data Augmentation for Neural Machine Translation using Generative Language Model—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus—0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR—0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More—0
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups—0
Data Augmentation with Dual Training for Offensive Span Detection—0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving—0
FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism—0
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)—0
Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining—0
Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information—0
Data-Driven Portfolio Management for Motion Pictures Industry: A New Data-Driven Optimization Methodology Using a Large Language Model as the Expert—0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
Data Efficacy for Language Model Training—0
Data-Efficient French Language Modeling with CamemBERTa—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning—0
Data Interpreter: An LLM Agent For Data Science—0
Data Metabolism: An Efficient Data Design Schema For Vision Language Model—0
BiMix: A Bivariate Data Mixing Law for Language Model Pretraining—0
Data Portraits: Recording Foundation Model Training Data—0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
Data Selection With Fewer Words—0
Dataset Cartography for Large Language Model Alignment: Mapping and Diagnosing Preference Data—0
Dataset Debt in Biomedical Language Modeling—0
Datasets for Multilingual Answer Sentence Selection—0
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms—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