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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10851–10900 of 17610 papers

TitleStatusHype
Reflective Verbal Reward Design for Pluralistic Alignment—0
Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia—0
REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing—0
Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain—0
RegionGPT: Towards Region Understanding Vision Language Model—0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning—0
Regularized Training of Nearest Neighbor Language Models—0
Regularizing activations in neural networks via distribution matching with the Wasserstein metric—0
Regularizing Text Categorization with Clusters of Words—0
Regular Patterns - Probably Approximately Correct Language Model—0
Rehearsal: Simulating Conflict to Teach Conflict Resolution—0
Reinforced Self-Training (ReST) for Language Modeling—0
Reinforcement Learning for Generative AI: A Survey—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Reinforcement Learning is all You Need—0
Reinforcement Pre-Training—0
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification—0
Reinforcing Thinking through Reasoning-Enhanced Reward Models—0
Relating Neural Text Degeneration to Exposure Bias—0
Relational Database Augmented Large Language Model—0
Relational Memory Augmented Language Models—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information—0
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition—0
Iterative Translation Refinement with Large Language Models—0
Iterative Value Function Optimization for Guided Decoding—0
"I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data—0
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models—0
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents—0
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization—0
It’s Basically the Same Language Anyway: the Case for a Nordic Language Model—0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization—0
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning—0
IUCL: Combining Information Sources for SemEval Task 5—0
IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation—0
I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection—0
IXA Biomedical Translation System at WMT16 Biomedical Translation Task—0
JABER and SABER: Junior and Senior Arabic BERt—0
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model—0
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model—0
JaFIn: Japanese Financial Instruction Dataset—0
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations—0
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models—0
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding—0
Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2—0
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization—0
Japanese Lexical Simplification for Non-Native Speakers—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