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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13151–13200 of 17610 papers

TitleStatusHype
Few-shot Subgoal Planning with Language Models—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network—0
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs—0
FewUser: Few-Shot Social User Geolocation via Contrastive Learning—0
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration—0
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference—0
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics—0
FGeo-TP: A Language Model-Enhanced Solver for Geometry Problems—0
F-HOI: Toward Fine-grained Semantic-Aligned 3D Human-Object Interactions—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
Field-Mediated Semantic Organization in Large Language Models: Evidence for Quantum-Like Properties in Artificial Neural Systems—0
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax Variance-Guided LLMs without Real Data Replay—0
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation—0
FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Filter bubbles and affective polarization in user-personalized large language model outputs—0
Finance Language Model Evaluation (FLaME)—0
Financial Knowledge Large Language Model—0
Financial News Analytics Using Fine-Tuned Llama 2 GPT Model—0
FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data—0
Finch: Prompt-guided Key-Value Cache Compression—0
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting—0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
Finding Pragmatic Differences Between Disciplines—0
Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction—0
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction—0
Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora—0
Finding Support Examples for In-Context Learning—0
Finding Syntax in Human Encephalography with Beam Search—0
Finding the Answers with Definition Models—0
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers—0
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model—0
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning—0
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
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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