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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5601–5650 of 17610 papers

TitleStatusHype
Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation—0
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-IdentificationCode0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Integration of LLM Quality Assurance into an NLG System—0
Is It Navajo? Accurate Language Detection in Endangered Athabaskan LanguagesCode0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning—0
BiFold: Bimanual Cloth Folding with Language Guidance—0
VLMaterial: Procedural Material Generation with Large Vision-Language Models—0
PRISMe: A Novel LLM-Powered Tool for Interactive Privacy Policy Assessment—0
MEL: Legal Spanish Language Model—0
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction—0
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare—0
Semantic Layered Embedding Diffusion in Large Language Models for Multi-Contextual Consistency—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets—0
Complete Chess Games Enable LLM Become A Chess Master—0
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning—0
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding—0
Multi-Grained Patch Training for Efficient LLM-based RecommendationCode0
Knowledge Graphs Construction from Criminal Court Appeals: Insights from the French Cassation Court—0
SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation—0
Locality-aware Fair Scheduling in LLM Serving—0
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game—0
LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration—0
Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment—0
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher EducationCode0
Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning—0
Humanity's Last Exam—0
Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval—0
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention—0
Multi-aspect Knowledge Distillation with Large Language ModelCode0
Large Language Model driven Policy Exploration for Recommender Systems—0
Communicating Activations Between Language Model Agents—0
EventVL: Understand Event Streams via Multimodal Large Language Model—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMsCode0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented RealityCode0
SoundSpring: Loss-Resilient Audio Transceiver with Dual-Functional Masked Language Modeling—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Large Language Model-Based Semantic Communication System for Image Transmission—0
LLM4WM: Adapting LLM for Wireless Multi-Tasking—0
MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model—0
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model—0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
Panoramic Interests: Stylistic-Content Aware Personalized Headline GenerationCode0
AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding—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