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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5201–5250 of 17610 papers

TitleStatusHype
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients—0
LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment ReportsCode0
Llama-3.1-Sherkala-8B-Chat: An Open Large Language Model for Kazakh—0
KurTail : Kurtosis-based LLM Quantization—0
Syntactic Learnability of Echo State Neural Language Models at Scale—0
Learning to Generate Long-term Future Narrations Describing Activities of Daily Living—0
WeightedKV: Attention Scores Weighted Key-Value Cache Merging for Large Language Models—0
Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation—0
FunBench: Benchmarking Fundus Reading Skills of MLLMs—0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Transformer Meets Twicing: Harnessing Unattended Residual InformationCode0
NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis—0
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy—0
Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs—0
Language Model Mapping in Multimodal Music Learning: A Grand Challenge Proposal—0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering—0
Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy—0
PinLanding: Content-First Keyword Landing Page Generation via Multi-Modal AI for Web-Scale Discovery—0
Can LLM Assist in the Evaluation of the Quality of Machine Learning Explanations?—0
Chronologically Consistent Large Language Models—0
Large Language Model-Based Benchmarking Experiment Settings for Evolutionary Multi-Objective Optimization—0
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation—0
MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model TrainingCode0
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication—0
Llamarine: Open-source Maritime Industry-specific Large Language Model—0
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training—0
KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model—0
Large Language Model Strategic Reasoning Evaluation through Behavioral Game Theory—0
M-LLM Based Video Frame Selection for Efficient Video Understanding—0
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research—0
Protecting multimodal large language models against misleading visualizationsCode0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Sparse Auto-Encoder Interprets Linguistic Features in Large Language Models—0
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook—0
Do Sparse Autoencoders Generalize? A Case Study of Answerability—0
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration—0
From Retrieval to Generation: Comparing Different Approaches—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
Collaborative Stance Detection via Small-Large Language Model Consistency VerificationCode0
Conformal Tail Risk Control for Large Language Model Alignment—0
DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
Evaluating Gender Bias in German Machine TranslationCode0
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity—0
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning—0
A City of Millions: Mapping Literary Social Networks At ScaleCode0
ANPMI: Assessing the True Comprehension Capabilities of LLMs for Multiple Choice Questions—0
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training—0
Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models—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