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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1201–1250 of 17610 papers

TitleStatusHype
LLM Agents for Education: Advances and Applications—0
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversionCode15
BriLLM: Brain-inspired Large Language Model—0
Text Compression for Efficient Language Generation—0
Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning—0
Reasoning-Grounded Natural Language Explanations for Language ModelsCode0
Open3DVQA: A Benchmark for Comprehensive Spatial Reasoning with Multimodal Large Language Model in Open SpaceCode1
TigerLLM -- A Family of Bangla Large Language ModelsCode0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Potential of large language model-powered nudges for promoting daily water and energy conservation—0
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction TuningCode0
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech TokensCode1
Generative Modeling for Mathematical DiscoveryCode2
LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems—0
NeurIPS 2023 LLM Efficiency Fine-tuning Competition—0
SCE: Scalable Consistency Ensembles Make Blackbox Large Language Model Generation More Reliable—0
Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More—0
MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis—0
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation—0
GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and EditingCode3
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
GroundingSuite: Measuring Complex Multi-Granular Pixel GroundingCode2
TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics—0
SmartWay: Enhanced Waypoint Prediction and Backtracking for Zero-Shot Vision-and-Language Navigation—0
Tempest: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search—0
PRISM: Preference Refinement via Implicit Scene Modeling for 3D Vision-Language Preference-Based Reinforcement Learning—0
Hybrid Agents for Image Restoration—0
OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problem with Reasoning Large Language ModelCode2
Toward a method for LLM-enabled Indoor Navigation—0
Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging—0
Medical Large Language Model Benchmarks Should Prioritize Construct Validity—0
Token Weighting for Long-Range Language ModelingCode0
Language-Enhanced Representation Learning for Single-Cell TranscriptomicsCode0
xVLM2Vec: Adapting LVLM-based embedding models to multilinguality using Self-Knowledge Distillation—0
PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs—0
Reinforcement Learning is all You Need—0
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability—0
BAMBI: Developing Baby Language Models for Italian—0
Why LLMs Cannot Think and How to Fix It—0
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo—0
Global Position Aware Group Choreography using Large Language Model—0
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language ModelsCode4
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action AlignmentCode3
NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language ModelCode0
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity DocumentsCode0
D3PO: Preference-Based Alignment of Discrete Diffusion Models—0
Bring Remote Sensing Object Detect Into Nature Language Model: Using SFT Method—0
Understanding the Quality-Diversity Trade-off in Diffusion Language ModelsCode0
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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