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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8851–8900 of 17610 papers

TitleStatusHype
Large Language Model for Science: A Study on P vs. NPCode0
Designs and Implementations in Neural Network-based Video Coding—0
Toward a Deeper Understanding: RetNet Viewed through ConvolutionCode0
Detecting Natural Language Biases with Prompt-based Learning—0
Improving Information Extraction on Business Documents with Specific Pre-Training TasksCode0
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling—0
Kani: A Lightweight and Highly Hackable Framework for Building Language Model ApplicationsCode2
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence LabelerCode0
Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple LevelsCode0
An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents—0
Decolonial AI Alignment: Openness, Viśesa-Dharma, and Including Excluded Knowledges—0
Towards LLM-based Autograding for Short Textual Answers—0
Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems—0
BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification—0
Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model—0
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual TokenizationCode2
MADLAD-400: A Multilingual And Document-Level Large Audited DatasetCode0
Knowledge Distillation-Empowered Digital Twin for Anomaly Detection—0
End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model PretrainingCode0
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment—0
LLMCad: Fast and Scalable On-device Large Language Model Inference—0
NESTLE: a No-Code Tool for Statistical Analysis of Legal CorpusCode0
Exploring an LM to generate Prolog Predicates from Mathematics Questions—0
An Anchor Learning Approach for Citation Field Learning—0
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social MediaCode1
LanSER: Language-Model Supported Speech Emotion Recognition—0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Evaluation of large language models for discovery of gene set functionCode1
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in VietnameseCode0
GPT Can Solve Mathematical Problems Without a CalculatorCode2
CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model—0
Automated Bioinformatics Analysis via AutoBACode2
Certifying LLM Safety against Adversarial PromptingCode1
Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and ResponsesCode0
AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW BasisCode0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Language Models for Novelty Detection in System Call Traces—0
PromptTTS 2: Describing and Generating Voices with Text Prompt—0
Physically Grounded Vision-Language Models for Robotic Manipulation—0
On the Planning, Search, and Memorization Capabilities of Large Language Models—0
Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content—0
nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited ResourcesCode3
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction TuningCode2
Code Representation Pre-training with Complements from Program Executions—0
Are Emergent Abilities in Large Language Models just In-Context Learning?Code1
Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy ConstructionCode0
Zero-shot information extraction from radiological reports using ChatGPT—0
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill 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