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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9901–9950 of 17610 papers

TitleStatusHype
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding—0
OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context—0
On a Benefit of Masked Language Model Pretraining: Robustness to Simplicity Bias—0
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias—0
On Accurate Evaluation of GANs for Language Generation—0
On Adversarial Examples for Biomedical NLP Tasks—0
Automatic Text Extractive Summarization Based on Graph and Pre-trained Language Model Attention—0
Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants—0
OncoGPT: A Medical Conversational Model Tailored with Oncology Domain Expertise on a Large Language Model Meta-AI (LLaMA)—0
On Conditional and Compositional Language Model Differentiable Prompting—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
On decoder-only architecture for speech-to-text and large language model integration—0
On-Demand Distributional Semantic Distance and Paraphrasing—0
OneDiff: A Generalist Model for Image Difference Captioning—0
One Epoch Is All You Need—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
On Elastic Language Models—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding—0
On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects—0
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities—0
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities—0
On Fairness of Unified Multimodal Large Language Model for Image Generation—0
On Functional Activations in Deep Neural Networks—0
On Improving Deep Learning Trace Analysis with System Call Arguments—0
On Improving Informativity and Grammaticality for Multi-Sentence Compression—0
On Language Model Integration for RNN Transducer based Speech Recognition—0
On Languaging a Simulation Engine—0
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data—0
On Learning Universal Representations Across Languages—0
Online Infix Probability Computation for Probabilistic Finite Automata—0
Online optimisation of log-linear weights in interactive machine translation—0
Online Representation Learning in Recurrent Neural Language Models—0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
On Measuring Social Biases in Prompt-Based Learning—0
On Mechanistic Circuits for Extractive Question-Answering—0
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer—0
On Modeling Sense Relatedness in Multi-prototype Word Embedding—0
On Modular Training of Neural Acoustics-to-Word Model for LVCSR—0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
On Multiplicative Integration with Recurrent Neural Networks—0
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots—0
On Privacy and Confidentiality of Communications in Organizational Graphs—0
On Randomized Classification Layers and Their Implications in Natural Language Generation—0
On Reducing Repetition in Abstractive Summarization—0
On Retrieval Augmentation and the Limitations of Language Model Training—0
On Reward Maximization and Distribution Matching for Fine-Tuning Language Models—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