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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8701–8750 of 17610 papers

TitleStatusHype
The Future of Scientific Publishing: Automated Article Generation—0
The Role of Language Imbalance in Cross-lingual Generalisation: Insights from Cloned Language ExperimentsCode0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction—0
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios—0
Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck—0
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions—0
Audio Dialogues: Dialogues dataset for audio and music understanding—0
Human Latency Conversational Turns for Spoken Avatar Systems—0
Data-Driven Portfolio Management for Motion Pictures Industry: A New Data-Driven Optimization Methodology Using a Large Language Model as the Expert—0
Auctions with LLM Summaries—0
Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs—0
A Multi-Expert Large Language Model Architecture for Verilog Code Generation—0
Improving Language Model Reasoning with Self-motivated Learning—0
BRAVE: Broadening the visual encoding of vision-language models—0
Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agents—0
Continuous Language Model Interpolation for Dynamic and Controllable Text GenerationCode0
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination—0
Toward Cross-Layer Energy Optimizations in AI Systems—0
MedRG: Medical Report Grounding with Multi-modal Large Language Model—0
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic ProvingCode0
We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard TextCode0
AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
Anchor-based Robust Finetuning of Vision-Language Models—0
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HDCode0
GUIDE: Graphical User Interface Data for Execution—0
Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis—0
Less is More for Improving Automatic Evaluation of Factual Consistency—0
On the Effect of (Near) Duplicate Subwords in Language ModellingCode0
LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding—0
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation—0
Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset—0
Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems—0
SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model SafetyCode0
OPSD: an Offensive Persian Social media Dataset and its baseline evaluations—0
Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security—0
LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Is English the New Programming Language? How About Pseudo-code Engineering?—0
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model—0
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training—0
Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients—0
Guiding Large Language Models to Generate Computer-Parsable Content—0
360^REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent SystemCode0
Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning—0
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling—0
Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Logic Chain Injection—0
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse—0
SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning—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