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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9301–9350 of 17610 papers

TitleStatusHype
Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation—0
Letter N-Gram-based Input Encoding for Continuous Space Language Models—0
Let the CAT out of the bag: Contrastive Attributed explanations for Text—0
Let the Code LLM Edit Itself When You Edit the Code—0
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation—0
Leveraging BERT Language Model for Arabic Long Document Classification—0
Leveraging vision-language models for fair facial attribute classification—0
Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims—0
Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs—0
運用概念模型化技術於中文大詞彙連續語音辨識之語言模型調適 (Leveraging Concept Modeling Techniques for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
Leveraging Contextual Information for Effective Entity Salience Detection—0
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic—0
Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—0
Leveraging Explicit Procedural Instructions for Data-Efficient Action Prediction—0
Leveraging Generative AI: Improving Software Metadata Classification with Generated Code-Comment Pairs—0
Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning—0
Leveraging High-Level Synthesis and Large Language Models to Generate, Simulate, and Deploy a Uniform Random Number Generator Hardware Design—0
Leveraging In-Context Learning for Language Model Agents—0
Leveraging Information Bottleneck for Scientific Document Summarization—0
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning—0
Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging—0
Leveraging Knowledge in Multilingual Commonsense Reasoning—0
Leveraging known Semantics for Spelling Correction—0
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation—0
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition—0
Leveraging Language Models to Detect Greenwashing—0
Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study—0
Leveraging Large Language Model as Simulated Patients for Clinical Education—0
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation—0
Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms—0
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature—0
Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study—0
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval—0
Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce—0
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce—0
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval—0
Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation—0
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT—0
Leveraging LLM Agents for Translating Network Configurations—0
Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions—0
Leveraging MoE-based Large Language Model for Zero-Shot Multi-Task Semantic Communication—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
Text Descriptions are Compressive and Invariant Representations for Visual 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