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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1701–1750 of 17610 papers

TitleStatusHype
Rationalization Models for Text-to-SQL—0
μnit Scaling: Simple and Scalable FP8 LLM Training—0
HSI: Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language ModelsCode0
Investigating Compositional Reasoning in Time Series Foundation Models—0
Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails—0
Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform—0
DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot ControlCode1
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks—0
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization—0
Enabling Autoregressive Models to Fill In Masked Tokens—0
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education—0
RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care—0
UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and UnderstandingCode1
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging—0
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech SystemCode11
The Complexity of Learning Sparse Superposed Features with Feedback—0
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
Gemstones: A Model Suite for Multi-Faceted Scaling LawsCode1
Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and StructureCode0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
Agentic Reasoning: Reasoning LLMs with Tools for the Deep ResearchCode0
Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context AccurayCode3
Concept Navigation and Classification via Open-Source Large Language Model Processing—0
Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning—0
Position-aware Automatic Circuit DiscoveryCode1
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth ApproachCode4
Learning the Language of NVMe Streams for Ransomware Detection—0
Robotouille: An Asynchronous Planning Benchmark for LLM AgentsCode1
Adaptive Semantic Prompt Caching with VectorQ—0
Verifiable Format Control for Large Language Model Generations—0
FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing—0
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference OptimizationCode2
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces—0
Vision-Integrated LLMs for Autonomous Driving Assistance : Human Performance Comparison and Trust Evaluation—0
Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device AgentsCode1
ChamaleonLLM: Batch-Aware Dynamic Low-Rank Adaptation via Inference-Time ClustersCode0
ADIFF: Explaining audio difference using natural languageCode1
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation—0
Ola: Pushing the Frontiers of Omni-Modal Language ModelCode3
RWKV-UI: UI Understanding with Enhanced Perception and Reasoning—0
WaferLLM: Large Language Model Inference at Wafer ScaleCode2
Multi-agent Architecture Search via Agentic SupernetCode3
Great Models Think Alike and this Undermines AI OversightCode1
Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning DynamicsCode1
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2—0
A Contemporary Survey of Large Language Model Assisted Program Analysis—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training—0
On Fairness of Unified Multimodal Large Language Model for Image Generation—0
Do Large Language Model Benchmarks Test Reliability?Code1
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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