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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10501–10550 of 17610 papers

TitleStatusHype
Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset—0
Progressive Class Semantic Matching for Semi-supervised Text Classification—0
Progressively Label Enhancement for Large Language Model Alignment—0
Projection of Turn Completion in Incremental Spoken Dialogue Systems—0
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines—0
Promises, Outlooks and Challenges of Diffusion Language Modeling—0
Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection—0
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
Prompt-based Depth Pruning of Large Language Models—0
Prompt-based System for Personality and Interpersonal Reactivity Prediction—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models—0
PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM—0
Prompt Distribution Learning—0
Prompt, Divide, and Conquer: Bypassing Large Language Model Safety Filters via Segmented and Distributed Prompt Processing—0
Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis—0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—0
PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines—0
Prompt-free and Efficient Language Model Fine-Tuning—0
Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM—0
Prompt-Guided Injection of Conformation to Pre-trained Protein Model—0
Prompt-Guided Turn-Taking Prediction—0
PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows—0
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages—0
Prompting as Multimodal Fusing—0
Prompting for a conversation: How to control a dialog model?—0
Prompting for Multimodal Hateful Meme Classification—0
Prompting Large Language Model for Machine Translation: A Case Study—0
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia—0
Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition—0
Prompting Large Language Models With the Socratic Method—0
Prompting PaLM for Translation: Assessing Strategies and Performance—0
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking—0
Prompt-Learning for Fine-Grained Entity Typing—0
Prompt-Learning for Fine-Grained Entity Typing—0
Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art—0
Prompt Optimization with Logged Bandit Data—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics—0
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques—0
Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review—0
Prompt-Reverse Inconsistency: LLM Self-Inconsistency Beyond Generative Randomness and Prompt Paraphrasing—0
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels—0
Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control—0
PromptSum: Parameter-Efficient Controllable Abstractive Summarization—0
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models—0
PromptTTS 2: Describing and Generating Voices with Text Prompt—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