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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4251–4300 of 17610 papers

TitleStatusHype
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge—0
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition—0
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes—0
CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration—0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering—0
Cloning Ideology and Style using Deep Learning—0
Closing Brackets with Recurrent Neural Networks—0
ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection—0
CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels—0
CLPLM: Character Level Pretrained Language Model for Extracting Support Phrases for Sentiment Labels—0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer—0
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation—0
CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Clustering and Median Aggregation Improve Differentially Private Inference—0
Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling—0
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings—0
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction—0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification—0
CoAM: Corpus of All-Type Multiword Expressions—0
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
CoAVT: A Cognition-Inspired Unified Audio-Visual-Text Pre-Training Model for Multimodal Processing—0
CobaltF: A Fluent Metric for MT Evaluation—0
The Curious Case of Class Accuracy Imbalance in LLMs: Post-hoc Debiasing via Nonlinear Integer Programming—0
CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation—0
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising—0
Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection—0
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code—0
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code—0
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio—0
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms—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