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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4501–4550 of 17610 papers

TitleStatusHype
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model—0
The Traitors: Deception and Trust in Multi-Agent Language Model SimulationsCode0
Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SpatialLLM: From Multi-modality Data to Urban Spatial IntelligenceCode0
Krikri: Advancing Open Large Language Models for Greek—0
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks—0
On the Thinking-Language Modeling Gap in Large Language Models—0
ORQA: A Benchmark and Foundation Model for Holistic Operating Room Modeling—0
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models—0
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video UnderstandingCode0
Structure-Aware Corpus Construction and User-Perception-Aligned Metrics for Large-Language-Model Code Completion—0
ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model—0
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation—0
VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation—0
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection—0
CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling—0
A Physics-Inspired Optimizer: Velocity Regularized Adam—0
IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment—0
CIE: Controlling Language Model Text Generations Using Continuous SignalsCode0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
A*-Decoding: Token-Efficient Inference Scaling—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems—0
From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling—0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant AnnotationsCode0
mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model—0
NeuroGen: Neural Network Parameter Generation via Large Language Models—0
Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering—0
Self-Destructive Language Model—0
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems—0
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment—0
SOCIA: An End-to-End Agentic Framework for Automated Cyber-Physical-Social Simulator Generation—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data—0
Reasoning Large Language Model Errors Arise from Hallucinating Critical Problem FeaturesCode0
PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging—0
LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades—0
Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission—0
An Explanation of Intrinsic Self-Correction via Linear Representations and Latent Concepts—0
CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction—0
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution BehaviorsCode0
Chain-of-Model Learning for Language ModelCode0
Efficiently Building a Domain-Specific Large Language Model from Scratch: A Case Study of a Classical Chinese Large Language Model—0
Maximizing Asynchronicity in Event-based Neural Networks—0
Low-Resource Language Processing: An OCR-Driven Summarization and Translation PipelineCode0
On DeepSeekMoE: Statistical Benefits of Shared Experts and Normalized Sigmoid Gating—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