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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7151–7200 of 17610 papers

TitleStatusHype
Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Fine-grained Preference Optimization Improves Zero-shot Text-to-Speech—0
Fine-Grained Propaganda Detection with Fine-Tuned BERT—0
Fine-Grained Self-Endorsement Improves Factuality and Reasoning—0
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model—0
FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores—0
FineText: Text Classification via Attention-based Language Model Fine-tuning—0
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology—0
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Reducing Non-Normative Text Generation from Language Models—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Fine-tuning ChatGPT for Automatic Scoring—0
Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach—0
Fine-tuning language models to find agreement among humans with diverse preferences—0
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization—0
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models—0
Fine-tuning LLaMA 2 interference: a comparative study of language implementations for optimal efficiency—0
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network—0
Fine-tuning of Language Models with Discriminator—0
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization—0
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints—0
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints—0
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction—0
Finnish Language Modeling with Deep Transformer Models—0
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model—0
First numerical observation of the Berezinskii-Kosterlitz-Thouless transition in language models—0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
Fish-bone diagram of research issue: Gain a bird's-eye view on a specific research topic—0
Fisher Flow Matching for Generative Modeling over Discrete Data—0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search—0
Fixed-Point Performance Analysis of Recurrent Neural Networks—0
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation—0
Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics—0
Flamb\'e: A Customizable Framework for Machine Learning Experiments—0
FLAME: A small language model for spreadsheet formulas—0
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement—0
FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference—0
Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference—0
FlashDecoding++: Faster Large Language Model Inference on GPUs—0
Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput—0
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model—0
FleetX—0
FlexCap: Describe Anything in Images in Controllable Detail—0
Flexible Frame Selection for Efficient Video Reasoning—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