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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11601–11650 of 17610 papers

TitleStatusHype
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation—0
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis—0
Rethinking Exposure Bias In Language Modeling—0
Rethinking Full Connectivity in Recurrent Neural Networks—0
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension—0
Rethinking Homogeneity of Vision and Text Tokens in Large Vision-and-Language Models—0
Reward Generalization in RLHF: A Topological Perspective—0
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective—0
Re-Thinking Inverse Graphics With Large Language Models—0
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Explaining Length Bias in LLM-Based Preference Evaluations—0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach—0
Rethinking Memory and Communication Cost for Efficient Large Language Model Training—0
Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective—0
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models—0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—0
Rethinking Text Line Recognition Models—0
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data—0
ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model—0
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies—0
Retrieval Augmentation for T5 Re-ranker using External Sources—0
Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models—0
Retrieval Augmented End-to-End Spoken Dialog Models—0
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support—0
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps—0
Retrieval Augmented Generation for Domain-specific Question Answering—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
Retrieval Augmented Language Model Pre-Training—0
Retrieval-Augmented Multimodal Language Modeling—0
Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering—0
Retrieval Augmented Spelling Correction for E-Commerce Applications—0
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Retrieval-based Video Language Model for Efficient Long Video Question Answering—0
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities—0
Retrieval-Enhanced Named Entity Recognition—0
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation—0
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion—0
Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval—0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation—0
Description-Based Text Similarity—0
Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning—0
Retrieving Visual Facts For Few-Shot Visual Question Answering—0
Retrofitting Structure-aware Transformer Language Model for End Tasks—0
Revealing Language Model Trajectories via Kullback-Leibler Divergence—0
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges—0
Reverse-engineering Language: A Study on the Semantic Compositionality of German Compounds—0
Reverse Prompt Engineering—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