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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14101–14150 of 17610 papers

TitleStatusHype
Uncovering Latent Human Wellbeing in Language Model Embeddings—0
Uncovering mesa-optimization algorithms in Transformers—0
Uncovering Overfitting in Large Language Model Editing—0
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning—0
Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems—0
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations—0
Understanding and Mitigating Tokenization Bias in Language Models—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment—0
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training—0
Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions—0
Understanding Emails and Drafting Responses -- An Approach Using GPT-3—0
Understanding In-Context Learning with a Pelican Soup Framework—0
Understanding language-elicited EEG data by predicting it from a fine-tuned language model—0
Understanding Learning Dynamics Of Language Models with SVCCA—0
Understanding LLMs: A Comprehensive Overview from Training to Inference—0
Understanding Political Polarisation using Language Models: A dataset and method—0
Understanding prompt engineering may not require rethinking generalization—0
Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets—0
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment—0
Understanding Semantics from Speech Through Pre-training—0
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples—0
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models—0
Understanding the Dataset Practitioners Behind Large Language Model Development—0
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention—0
Understanding the Inner Workings of Language Models Through Representation Dissimilarity—0
Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT—0
Understanding the Logical and Semantic Structure of Large Documents—0
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model—0
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision—0
Understanding the performance gap between online and offline alignment algorithms—0
Understanding Sarcoidosis Using Large Language Models and Social Media Data—0
Understanding the role of FFNs in driving multilingual behaviour in LLMs—0
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology—0
Understanding Token Probability Encoding in Output Embeddings—0
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation—0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration—0
Relationship of the language distance to English ability of a country—0
Relations Prediction for Knowledge Graph Completion using Large Language Models—0
RelationVLM: Making Large Vision-Language Models Understand Visual Relations—0
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection—0
Relative Overfitting and Accept-Reject Framework—0
Relaxed Attention for Transformer Models—0
Relaxed Softmax for learning from Positive and Unlabeled data—0
Release Strategies and the Social Impacts of Language Models—0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
Relevance-Promoting Language Model for Short-Text Conversation—0
Rel-grams: A Probabilistic Model of Relations in Text—0
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge—0
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation—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