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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9501–9550 of 17610 papers

TitleStatusHype
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models—0
Like a Baby: Visually Situated Neural Language Acquisition—0
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning—0
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
Unlearnable Algorithms for In-context Learning—0
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model—0
Word Vector/Conditional Random Field-based Chinese Spelling Error Detection for SIGHAN-2015 Evaluation—0
Visual Speech Language Models—0
XAI for All: Can Large Language Models Simplify Explainable AI?—0
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection—0
Visual Text Generation in the Wild—0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension—0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble—0
ViTOC: Vision Transformer and Object-aware Captioner—0
VITRO: Vocabulary Inversion for Time-series Representation Optimization—0
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source—0
WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist—0
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT—0
VL-BEiT: Generative Vision-Language Pretraining—0
Will GPT-4 Run DOOM?—0
A Survey on Large Language Model-empowered Autonomous Driving—0
VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration—0
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection—0
Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development—0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions—0
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning—0
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture—0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision—0
VLMAE: Vision-Language Masked Autoencoder—0
VL-Mamba: Exploring State Space Models for Multimodal Learning—0
VLMaterial: Procedural Material Generation with Large Vision-Language Models—0
VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis—0
VLMine: Long-Tail Data Mining with Vision Language Models—0
VLM-KD: Knowledge Distillation from VLM for Long-Tail Visual Recognition—0
VLM-PL: Advanced Pseudo Labeling Approach for Class Incremental Object Detection via Vision-Language Model—0
Unsupervised Domain Adaptation of Language Models for Reading Comprehension—0
VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation—0
VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model—0
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding—0
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation—0
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis—0
Unsupervised Bias Detection in College Student Newspapers—0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
EVJVQA Challenge: Multilingual Visual Question Answering—0
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning—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