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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1251–1300 of 17610 papers

TitleStatusHype
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models—0
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study—0
OASIS: Order-Augmented Strategy for Improved Code Search—0
Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations—0
EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability TreesCode1
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
LongProLIP: A Probabilistic Vision-Language Model with Long Context TextCode2
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback—0
Accelerating MoE Model Inference with Expert Sharding—0
Mellow: a small audio language model for reasoningCode2
BiasEdit: Debiasing Stereotyped Language Models via Model EditingCode1
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model AdaptationCode0
EditLord: Learning Code Transformation Rules for Code Editing—0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph—0
MapQA: Open-domain Geospatial Question Answering on Map Data—0
EAZY: Eliminating Hallucinations in LVLMs by Zeroing out Hallucinatory Image Tokens—0
LLMIdxAdvis: Resource-Efficient Index Advisor Utilizing Large Language Model—0
Evaluating LLaMA 3.2 for Software Vulnerability Detection—0
GRITHopper: Decomposition-Free Multi-Hop Dense RetrievalCode1
Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection—0
V2Flow: Unifying Visual Tokenization and Large Language Model Vocabularies for Autoregressive Image GenerationCode1
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model—0
Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs—0
When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token PruningCode2
Towards Fine-Grained Video Question Answering—0
Building English ASR model with regional language support—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Effect of Selection Format on LLM Performance—0
When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma—0
DiffCLIP: Differential Attention Meets CLIPCode2
Gender Encoding Patterns in Pretrained Language Model RepresentationsCode0
Multimodal Programming in Computer Science with Interactive Assistance Powered by Large Language Model—0
Seesaw: High-throughput LLM Inference via Model Re-sharding—0
CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model—0
AI-Facilitated Episodic Future Thinking For Adults with Obesity—0
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model—0
Image is All You Need: Towards Efficient and Effective Large Language Model-Based Recommender Systems—0
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models—0
Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best PracticesCode1
Language Model Personalization via Reward Factorization—0
VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene CompletionCode1
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding—0
Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language ModelCode2
Phraselette: A Poet's Procedural Palette—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Language modelling techniques for analysing the impact of human genetic variation—0
SpecServe: Efficient and SLO-Aware Large Language Model Serving with Adaptive Speculative Decoding—0
Revitalizing Saturated Benchmarks: A Weighted Metric Approach for Differentiating Large Language Model Performance—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