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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14201–14250 of 17610 papers

TitleStatusHype
Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating—0
Resolving Pronouns for a Resource-Poor Language, Malayalam Using Resource-Rich Language, Tamil.—0
Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration—0
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval—0
Resource Evaluation for Usable Speech Interfaces: Utilizing Human-Human Dialogue—0
ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents—0
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction—0
Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities—0
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent—0
Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation—0
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality—0
Results of the WMT15 Tuning Shared Task—0
ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model—0
Rethinking Client Reweighting for Selfish Federated Learning—0
Rethinking Controllable Variational Autoencoders—0
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling—0
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
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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