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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2201–2250 of 17610 papers

TitleStatusHype
Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain TestingCode1
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
LLM4AD: A Platform for Algorithm Design with Large Language ModelCode4
FedTLU: Federated Learning with Targeted Layer Updates—0
Large Language Model Safety: A Holistic SurveyCode2
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion—0
Autoregressive Speech Synthesis with Next-Distribution Prediction—0
A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation—0
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language ModelsCode0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story VideosCode0
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
Online Preference-based Reinforcement Learning with Self-augmented Feedback from Large Language ModelCode1
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately—0
Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension—0
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults—0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
Application of Multimodal Large Language Models in Autonomous Driving—0
The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents—0
Technical Report: Small Language Model for Japanese Clinical and Medicine—0
Correcting Large Language Model Behavior via Influence Function—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems—0
Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information—0
Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
PolySmart and VIREO @ TRECVid 2024 Ad-hoc Video Search—0
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics—0
VirusT5: Harnessing Large Language Models to Predicting SARS-CoV-2 EvolutionCode0
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning—0
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research—0
Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model—0
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage—0
QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning—0
Continual Learning Using Only Large Language Model Prompting—0
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and DetectionCode1
Frequency Is What You Need: Word-frequency Masking Benefits Vision-Language Model Pre-trainingCode0
J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM—0
ConfliBERT: A Language Model for Political ConflictCode1
Automated Root Cause Analysis System for Complex Data Products—0
HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages—0
Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering—0
DirectorLLM for Human-Centric Video Generation—0
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision—0
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning—0
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task LinkageCode0
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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