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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4351–4400 of 17610 papers

TitleStatusHype
ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools—0
A Lightweight Multi-Expert Generative Language Model System for Engineering Information and Knowledge Extraction—0
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion—0
Creativity in LLM-based Multi-Agent Systems: A Survey—0
The Multilingual Divide and Its Impact on Global AI Safety—0
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short OnesCode0
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective—0
StreamLink: Large-Language-Model Driven Distributed Data Engineering System—0
PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective—0
LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer—0
MSD-LLM: Predicting Ship Detention in Port State Control Inspections with Large Language Model—0
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs—0
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking AgentCode0
Learning to Select In-Context Demonstration Preferred by Large Language Model—0
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation LearningCode0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety—0
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction—0
SeMe: Training-Free Language Model Merging via Semantic Alignment—0
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models—0
VSCBench: Bridging the Gap in Vision-Language Model Safety CalibrationCode0
Attention! You Vision Language Model Could Be Maliciously Manipulated—0
Balancing Computation Load and Representation Expressivity in Parallel Hybrid Neural Networks—0
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining—0
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
Causal Distillation: Transferring Structured Explanations from Large to Compact Language Models—0
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?—0
Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language ModelsCode0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence MaskingCode0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model—0
In-context Language Learning for Endangered Languages in Speech Recognition—0
FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)—0
Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator—0
Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model—0
Evaluating Steering Techniques using Human Similarity Judgments—0
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing—0
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models—0
Meta-aware Learning in text-to-SQL Large Language Model—0
Towards Reliable Large Audio Language Model—0
Paying Alignment Tax with Contrastive Learning—0
OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language Model—0
The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework—0
metaTextGrad: Automatically optimizing language model optimizers—0
REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing—0
LLM-QFL: Distilling Large Language Model for Quantum Federated LearningCode0
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications—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