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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11051–11100 of 17610 papers

TitleStatusHype
Vision Language Transformers: A Survey—0
Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment—0
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical RecordsCode0
Deductive Additivity for Planning of Natural Language ProofsCode0
Generative Job Recommendations with Large Language Model—0
Evade ChatGPT Detectors via A Single Space—0
Emoji Prediction in Tweets using BERTCode0
A Language Model for Grammatical Error Correction in L2 Russian—0
Garbage in, garbage out: Zero-shot detection of crime using Large Language ModelsCode0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
On Conditional and Compositional Language Model Differentiable Prompting—0
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases—0
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding—0
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking—0
ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience—0
ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis—0
Conformer LLMs -- Convolution Augmented Large Language Models—0
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
CephGPT-4: An Interactive Multimodal Cephalometric Measurement and Diagnostic System with Visual Large Language Model—0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment—0
InstructEval: Systematic Evaluation of Instruction Selection Methods—0
Biomedical Language Models are Robust to Sub-optimal TokenizationCode0
Stay on topic with Classifier-Free Guidance—0
Ticket-BERT: Labeling Incident Management Tickets with Language Models—0
RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot—0
HPC-Coder: Modeling Parallel Programs using Large Language Models—0
Towards Open-Domain Topic Classification—0
A Hybrid System for Systematic Generalization in Simple Arithmetic ProblemsCode0
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?—0
Benchmarking Large Language Model Capabilities for Conditional Generation—0
Pareto Optimal Learning for Estimating Large Language Model Errors—0
An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection—0
Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition—0
Let Segment Anything Help Image Dehaze—0
S2SNet: A Pretrained Neural Network for Superconductivity DiscoveryCode0
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool—0
Using Large Language Models to Provide Explanatory Feedback to Human Tutors—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention—0
Automatic Assessment of Divergent Thinking in Chinese Language with TransDis: A Transformer-Based Language Model Approach—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
LM4HPC: Towards Effective Language Model Application in High-Performance Computing—0
Self-Supervised Image Captioning with CLIP—0
LongCoder: A Long-Range Pre-trained Language Model for Code Completion—0
Large Multimodal Models: Notes on CVPR 2023 Tutorial—0
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary—0
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and PlacementCode0
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs—0
Low-Rank Prune-And-Factorize for Language Model Compression—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