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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7551–7600 of 17610 papers

TitleStatusHype
StarVector: Generating Scalable Vector Graphics Code from Images and TextCode5
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social MediaCode0
LLM-Twin: Mini-Giant Model-driven Beyond 5G Digital Twin Networking Framework with Semantic Secure Communication and Computation—0
A Unified Framework for Multi-Domain CTR Prediction via Large Language ModelsCode1
Knowledge Trees: Gradient Boosting Decision Trees on Knowledge Neurons as Probing Classifier—0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion—0
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability—0
Demystifying Instruction Mixing for Fine-tuning Large Language ModelsCode0
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion—0
Mixed Distillation Helps Smaller Language Model Better Reasoning—0
PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPUCode5
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated ContentCode0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
Paloma: A Benchmark for Evaluating Language Model Fit—0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
Resolving Crash Bugs via Large Language Models: An Empirical Study—0
Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document GenerationCode1
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language ModelsCode1
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent—0
Context-Driven Interactive Query Simulations Based on Generative Large Language ModelsCode0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
Student as an Inherent Denoiser of Noisy Teacher—0
LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language—0
Catwalk: A Unified Language Model Evaluation Framework for Many DatasetsCode1
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition—0
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style PluginCode2
InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs—0
Osprey: Pixel Understanding with Visual Instruction TuningCode4
Challenges with unsupervised LLM knowledge discovery—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach—0
Successor Heads: Recurring, Interpretable Attention Heads In The Wild—0
Holodeck: Language Guided Generation of 3D Embodied AI EnvironmentsCode2
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward HackingCode1
Pixel Aligned Language Models—0
CogAgent: A Visual Language Model for GUI AgentsCode5
Personalized Autonomous Driving with Large Language Models: Field ExperimentsCode1
TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model ReasoningCode1
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and GenerationCode1
Language Modeling on a SpiNNaker 2 Neuromorphic Chip—0
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers—0
Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language InferenceCode0
Modeling Complex Mathematical Reasoning via Large Language Model based MathAgentCode1
Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language modelCode1
Assessing GPT4-V on Structured Reasoning Tasks—0
Synocene, Beyond the Anthropocene: De-Anthropocentralising Human-Nature-AI Interaction—0
Contractive error feedback for gradient compression—0
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning—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