SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14151–14200 of 17610 papers

TitleStatusHype
RELIC: Investigating Large Language Model Responses using Self-Consistency—0
Remember what you did so you know what to do next—0
Reminding Multimodal Large Language Models of Object-aware Knowledge with Retrieved Tags—0
Reminding the Incremental Language Model via Data-Free Self-Distillation—0
Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model—0
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment—0
Remote Timing Attacks on Efficient Language Model Inference—0
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment—0
Repairing Bugs in Python Assignments Using Large Language Models—0
Repair Is Nearly Generation: Multilingual Program Repair with LLMs—0
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability—0
Repetition Facilitates Processing: The Processing Advantage of Construction Repetition in Dialogue—0
Rephrasing Electronic Health Records for Pretraining Clinical Language Models—0
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training—0
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling—0
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training—0
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents—0
Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling—0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
Representation Learning for Conversational Data using Discourse Mutual Information Maximization—0
Representation Learning for Conversational Data using Discourse Mutual Information Maximization—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Representation Learning in Geology and GilBERT—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model—0
Representative Social Choice: From Learning Theory to AI Alignment—0
Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models—0
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval—0
Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models—0
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Repurposing Decoder-Transformer Language Models for Abstractive Summarization—0
Reranking Machine Translation Hypotheses with Structured and Web-based Language Models—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT—0
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning—0
Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems—0
Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase—0
Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents—0
Research on Tibetan Tourism Viewpoints information generation system based on LLM—0
Reservoir Transformers—0
Residual Language Model for End-to-end Speech Recognition—0
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations—0
ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task—0
Resolving Crash Bugs via Large Language Models: An Empirical Study—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