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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2501–2550 of 17610 papers

TitleStatusHype
ARChef: An iOS-Based Augmented Reality Cooking Assistant Powered by Multimodal Gemini LLM—0
MusicGen-Chord: Advancing Music Generation through Chord Progressions and Interactive Web-UI—0
LLaMA-Gene: A General-purpose Gene Task Large Language Model Based on Instruction Fine-tuning—0
Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model—0
KV Shifting Attention Enhances Language ModelingCode2
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks—0
Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT—0
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 PatientsCode0
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads—0
Rephrasing Electronic Health Records for Pretraining Clinical Language Models—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows—0
VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising—0
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation—0
An AI-driven multimodal smart home platform for continuous monitoring and intelligent assistance in post-stroke patients—0
Marconi: Prefix Caching for the Era of Hybrid LLMs—0
Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease—0
Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models—0
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference—0
Verbalized Representation Learning for Interpretable Few-Shot GeneralizationCode0
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models—0
CoVis: A Collaborative Framework for Fine-grained Graphic Visual Understanding—0
FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model ServingCode7
Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image AnalysisCode1
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment—0
Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students—0
JPPO: Joint Power and Prompt Optimization for Accelerated Large Language Model Services—0
NewsEdits 2.0: Learning the Intentions Behind Updating News—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Diffusion Self-Distillation for Zero-Shot Customized Image Generation—0
VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis—0
R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge—0
Large Language Model-Brained GUI Agents: A SurveyCode3
Wearable intelligent throat enables natural speech in stroke patients with dysarthria—0
The Context of Crash Occurrence: A Complexity-Infused Approach Integrating Semantic, Contextual, and Kinematic Features—0
DiagramQG: A Dataset for Generating Concept-Focused Questions from Diagrams—0
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding—0
MotionLLaMA: A Unified Framework for Motion Synthesis and ComprehensionCode2
DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction—0
LongKey: Keyphrase Extraction for Long DocumentsCode1
STAR: Synthesis of Tailored Architectures—0
MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation—0
Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining—0
Pushing the Limits of Large Language Model Quantization via the Linearity TheoremCode3
On the Efficiency of NLP-Inspired Methods for Tabular Deep LearningCode3
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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