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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3751–3800 of 17610 papers

TitleStatusHype
ELMS: Elasticized Large Language Models On Mobile Devices—0
A Hetero-functional Graph Resilience Analysis for Convergent Systems-of-Systems—0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting—0
Interactive Machine Teaching by Labeling Rules and Instances—0
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?Code0
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality—0
POINTS: Improving Your Vision-language Model with Affordable Strategies—0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
Achieving Peak Performance for Large Language Models: A Systematic Review—0
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support—0
Sparse Rewards Can Self-Train Dialogue AgentsCode1
Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning—0
How Does Code Pretraining Affect Language Model Task Performance?—0
Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study—0
Multi-Programming Language Ensemble for Code Generation in Large Language ModelCode0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets—0
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding—0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
The AdEMAMix Optimizer: Better, Faster, OlderCode2
N-gram Prediction and Word Difference Representations for Language Modeling—0
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers—0
A Fused Large Language Model for Predicting Startup Success—0
LAST: Language Model Aware Speech Tokenization—0
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model—0
Irrelevant Alternatives Bias Large Language Model Hiring Decisions—0
ISO: Overlap of Computation and Communication within Seqenence For LLM Inference—0
MarS: a Financial Market Simulation Engine Powered by Generative Foundation ModelCode5
Creating Domain-Specific Translation Memories for Machine Translation Fine-tuning: The TRENCARD Bilingual Cardiology Corpus—0
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
Oddballness: universal anomaly detection with language models—0
Pre-training data selection for biomedical domain adaptation using journal impact metrics—0
Large Language Model-Based Agents for Software Engineering: A SurveyCode4
Historical German Text Normalization Using Type- and Token-Based Language Modeling—0
Standing on the Shoulders of Giants: Reprogramming Visual-Language Model for General Deepfake Detection—0
A Medical Multimodal Large Language Model for Pediatric Pneumonia—0
Accelerating Large Language Model Training with Hybrid GPU-based Compression—0
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models—0
RouterRetriever: Routing over a Mixture of Expert Embedding ModelsCode1
Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation—0
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling—0
Language Model Powered Digital Biology with BRADCode2
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis—0
Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT—0
Approximating mutual information of high-dimensional variables using learned representations—0
FuzzCoder: Byte-level Fuzzing Test via Large Language ModelCode1
An Implementation of Werewolf Agent That does not Truly Trust LLMs—0
Dynamic Motion Synthesis: Masked Audio-Text Conditioned Spatio-Temporal Transformers—0
SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration—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