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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 301–350 of 17610 papers

TitleStatusHype
Large Language Model Meets Constraint Propagation—0
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression RecognitionCode1
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
ATLAS: Learning to Optimally Memorize the Context at Test Time—0
CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model AgentsCode0
Learning Parametric Distributions from Samples and PreferencesCode0
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners—0
Discriminative Policy Optimization for Token-Level Reward ModelsCode0
VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality EvaluationCode1
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation—0
An Empirical Study of Federated Prompt Learning for Vision Language Model—0
SCORPIO: Serving the Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference—0
Augment or Not? A Comparative Study of Pure and Augmented Large Language Model RecommendersCode0
Dataset Cartography for Large Language Model Alignment: Mapping and Diagnosing Preference Data—0
TrackVLA: Embodied Visual Tracking in the Wild—0
PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents—0
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion—0
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent SystemsCode0
Spoken Language Modeling with Duration-Penalized Self-Supervised UnitsCode0
VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation—0
Beam-Guided Knowledge Replay for Knowledge-Rich Image Captioning using Vision-Language Model—0
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language ModelCode0
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreementCode0
3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model—0
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents—0
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles—0
ChatCFD: an End-to-End CFD Agent with Domain-specific Structured ThinkingCode1
ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage—0
Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction—0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning—0
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models—0
CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment—0
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative ClassificationCode0
Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging—0
Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems—0
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image GenerationCode0
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
A Tool for Generating Exceptional Behavior Tests With Large Language ModelsCode0
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems—0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding—0
Conversational Alignment with Artificial Intelligence in Context—0
Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment—0
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation—0
Zero-Shot Vision Encoder Grafting via LLM SurrogatesCode2
The Multilingual Divide and Its Impact on Global AI Safety—0
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective—0
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short OnesCode0
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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