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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7551–7600 of 17610 papers

TitleStatusHype
Studying the Effects of Cognitive Biases in Evaluation of Conversational Agents—0
Studying the impacts of pre-training using ChatGPT-generated text on downstream tasks—0
Studying the Role of Input-Neighbor Overlap in Retrieval-Augmented Language Models Training Efficiency—0
Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing—0
Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling—0
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding—0
StyleBERT: Chinese pretraining by font style information—0
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models—0
Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles—0
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples—0
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation—0
Style Variation as a Vantage Point for Code-Switching—0
Stylistic Variation in Television Dialogue for Natural Language Generation—0
Stylometry in a Bilingual Setup—0
Sub-character Neural Language Modelling in Japanese—0
Subformer: A Parameter Reduced Transformer—0
SubICap: Towards Subword-informed Image Captioning—0
Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities—0
Submix: Practical Private Prediction for Large-Scale Language Models—0
Submodularity for Data Selection in Machine Translation—0
Subsegmental language detection in Celtic language text—0
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training—0
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults—0
Subword and Crossword Units for CTC Acoustic Models—0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity—0
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling—0
Successor Features for Efficient Multisubject Controlled Text Generation—0
Successor Heads: Recurring, Interpretable Attention Heads In The Wild—0
Succinct Data Structures for NLP-at-Scale—0
sudoLLM : On Multi-role Alignment of Language Models—0
SudoLM: Learning Access Control of Parametric Knowledge with Authorization Alignment—0
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
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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