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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13201–13250 of 17610 papers

TitleStatusHype
Suffix Trees as Language Models—0
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain—0
SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark—0
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Superhuman performance of a large language model on the reasoning tasks of a physician—0
Supermind Ideator: Exploring generative AI to support creative problem-solving—0
SuperOCR for ALTA 2017 Shared Task—0
Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks—0
Supersense Tagging with a Combination of Character, Subword, and Word-level Representations—0
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research—0
Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech's Participation in the WMT 2018 Quality Estimation Task—0
Supervised classification of end-of-lines in clinical text with no manual annotation—0
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models—0
Supervised Sentence Fusion with Single-Stage Inference—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Supporting Human-AI Collaboration in Auditing LLMs with LLMs—0
Supporting Sensemaking of Large Language Model Outputs at Scale—0
Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt—0
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling—0
Suppressing Pink Elephants with Direct Principle Feedback—0
Surface Realization Using Pretrained Language Models—0
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Surfer100: Generating Surveys From Web Resources, Wikipedia-style—0
Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation—0
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence—0
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models—0
Surveying Generative AI's Economic Expectations—0
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization—0
Susceptibility to Influence of Large Language Models—0
Sustainable Modular Debiasing of Language Models—0
SUTA-LM: Bridging Test-Time Adaptation and Language Model Rescoring for Robust ASR—0
SUTRA: Scalable Multilingual Language Model Architecture—0
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning—0
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks—0
SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction—0
SwahBERT: Language Model of Swahili—0
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks—0
SWAN-GPT: An Efficient and Scalable Approach for Long-Context Language Modeling—0
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training—0
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework—0
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation—0
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment—0
Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications—0
Syllable-level Neural Language Model for Agglutinative Language—0
Symbolic Representation for Any-to-Any Generative Tasks—0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise—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