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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5001–5050 of 17610 papers

TitleStatusHype
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling—0
Detecting Text Reuse with Modified and Weighted N-grams—0
Detecting Unintended Memorization in Language-Model-Fused ASR—0
Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT—0
Caption Generation on Scenes with Seen and Unseen Object Categories—0
Detection of Criminal Texts for the Polish State Border Guard—0
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model—0
Detection of Somali-written Fake News and Toxic Messages on the Social Media Using Transformer-based Language Models—0
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
Determining the placement of German verbs in English--to--German SMT—0
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization—0
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data—0
Developing for personalised learning: the long road from educational objectives to development and feedback—0
Developing Healthcare Language Model Embedding Spaces—0
Developing Interactive Tourism Planning: A Dialogue Robot System Powered by a Large Language Model—0
Developing Language Resources and NLP Tools for the North Korean Language—0
Developing Partially-Transcribed Speech Corpus from Edited Transcriptions—0
Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches—0
Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Models—0
Development and Evaluation of Speech Recognition for the Welsh Language—0
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties—0
Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model—0
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments—0
Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection—0
Development of a Reliable and Accessible Caregiving Language Model (CaLM)—0
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic—0
Development of a Web-Scale Chinese Word N-gram Corpus with Parts of Speech Information—0
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training—0
Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease—0
DFKI's system for WMT16 IT-domain task, including analysis of systematic errors—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation—0
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching—0
Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine—0
DiagramQG: A Dataset for Generating Concept-Focused Questions from Diagrams—0
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever—0
Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic—0
Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021—0
Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs—0
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?—0
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation—0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Dialog Context Language Modeling with Recurrent Neural Networks—0
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder—0
Dialogue-Contextualized Re-ranking for Medical History-Taking—0
Dialogue Language Model with Large-Scale Persona Data Engineering—0
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition—0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation—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