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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9801–9850 of 17610 papers

TitleStatusHype
Measuring Gender and Racial Biases in Large Language Models—0
Measuring Gender Bias in West Slavic Language Models—0
Measuring Moral Inconsistencies in Large Language Models—0
Measuring Patent Claim Generation by Span Relevancy—0
Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model—0
Measuring Progress on Scalable Oversight for Large Language Models—0
Measuring Readability of Polish Texts: Baseline Experiments—0
Measuring reasoning capabilities of ChatGPT—0
Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy—0
Measuring temporal effects of agent knowledge by date-controlled tool use—0
Measuring the Carbon Intensity of AI in Cloud Instances—0
Measuring the Influence of Long Range Dependencies with Neural Network Language Models—0
Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?—0
Mechanistic evaluation of Transformers and state space models—0
Mechanistic Permutability: Match Features Across Layers—0
Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models—0
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization—0
MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at Scale—0
MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection—0
MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration—0
MEDBind: Unifying Language and Multimodal Medical Data Embeddings—0
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking—0
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation—0
MedGo: A Chinese Medical Large Language Model—0
MediaGPT : A Large Language Model For Chinese Media—0
MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines—0
Medical Large Language Model Benchmarks Should Prioritize Construct Validity—0
Counting Clinical Trials: New Evidence on Pharmaceutical Sector Productivity—0
MedRG: Medical Report Grounding with Multi-modal Large Language Model—0
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers—0
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis—0
Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias—0
MEGen: Generative Backdoor in Large Language Models via Model Editing—0
Megrez-Omni Technical Report—0
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP—0
MEL: Legal Spanish Language Model—0
Syllable-level lyrics generation from melody exploiting character-level language model—0
Meltemi: The first open Large Language Model for Greek—0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
Membership Inference on Word Embedding and Beyond—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
MemeBLIP2: A novel lightweight multimodal system to detect harmful memes—0
Memformer: A Memory-Augmented Transformer for Sequence Modeling—0
MemInsight: Autonomous Memory Augmentation for LLM Agents—0
Memorization-Compression Cycles Improve Generalization—0
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models—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