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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8476–8500 of 17610 papers

TitleStatusHype
Uncertainty Aware Learning for Language Model Alignment—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings—0
UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference—0
Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models—0
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks—0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization—0
Uncovering Factor Level Preferences to Improve Human-Model Alignment—0
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
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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