SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9551–9600 of 17610 papers

TitleStatusHype
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model—0
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection—0
Yandex School of Data Analysis Russian-English Machine Translation System for WMT14—0
Vocabulary Attack to Hijack Large Language Model Applications—0
Winning Solution For Meta KDD Cup' 24—0
Unleash GPT-2 Power for Event Detection—0
VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation—0
xVLM2Vec: Adapting LVLM-based embedding models to multilinguality using Self-Knowledge Distillation—0
WinoViz: Probing Visual Properties of Objects Under Different States—0
X-VILA: Cross-Modality Alignment for Large Language Model—0
World Models: The Safety Perspective—0
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks—0
Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features—0
VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model—0
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model—0
VoxRep: Enhancing 3D Spatial Understanding in 2D Vision-Language Models via Voxel Representation—0
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits—0
World Models with Hints of Large Language Models for Goal Achieving—0
VQ-T: RNN Transducers using Vector-Quantized Prediction Network States—0
VR-GPT: Visual Language Model for Intelligent Virtual Reality Applications—0
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis—0
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts—0
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models—0
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model—0
VU-BERT: A Unified framework for Visual Dialog—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—0
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model—0
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking—0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation—0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment—0
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence—0
XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model—0
WAFFLE: Multimodal Floorplan Understanding in the Wild—0
WSD for n-best reranking and local language modeling in SMT—0
WaLDORf: Wasteless Language-model Distillation On Reading-comprehension—0
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
Witscript 2: A System for Generating Improvised Jokes Without Wordplay—0
WalkVLM:Aid Visually Impaired People Walking by Vision Language Model—0
Witscript: A System for Generating Improvised Jokes in a Conversation—0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
Unleashing the Power of Large Language Model for Denoising Recommendation—0
WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training—0
WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction—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