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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3351–3400 of 17610 papers

TitleStatusHype
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style—0
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
Autoregressive Large Language Models are Computationally Universal—0
Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs—0
Function-Guided Conditional Generation Using Protein Language Models with Adapters—0
UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference—0
KidLM: Advancing Language Models for Children -- Early Insights and Future DirectionsCode0
No Need to Talk: Asynchronous Mixture of Language Models—0
Large Language Models can be Strong Self-Detoxifiers—0
Understanding Large Language Models in Your Pockets: Performance Study on COTS Mobile Devices—0
Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation—0
Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages—0
Parallel Corpus Augmentation using Masked Language Models—0
Autoregressive Action Sequence Learning for Robotic ManipulationCode2
Audio-Agent: Leveraging LLMs For Audio Generation, Editing and Composition—0
Permissive Information-Flow Analysis for Large Language Models—0
Scaling Parameter-Constrained Language Models with Quality Data—0
One2set + Large Language Model: Best Partners for Keyphrase GenerationCode0
Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks—0
An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation—0
BrainTransformers: SNN-LLM—0
A Dutch Financial Large Language ModelCode0
CaLMFlow: Volterra Flow Matching using Causal Language Models—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
Geometry is All You Need: A Unified Taxonomy of Matrix and Tensor Factorization for Compression of Generative Language Models—0
Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features—0
NNetscape Navigator: Complex Demonstrations for Web Agents Without a DemonstratorCode2
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning—0
Grounding Large Language Models In Embodied Environment With Imperfect World Models—0
Large Language Model Aided Multi-objective Evolutionary Algorithm: a Low-cost Adaptive Approach—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language ModelCode1
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and ObjectsCode1
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning—0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data—0
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences—0
Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL—0
General Preference Modeling with Preference Representations for Aligning Language ModelsCode1
Examining Language Modeling Assumptions Using an Annotated Literary Dialect CorpusCode0
On the Proper Treatment of Tokenization in PsycholinguisticsCode0
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation—0
Multi-modal clothing recommendation model based on large model and VAE enhancement—0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM InversionCode0
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems—0
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization—0
FAN: Fourier Analysis NetworksCode3
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent CollaborationCode1
Morphological evaluation of subwords vocabulary used by BETO language model—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