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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9351–9400 of 17610 papers

TitleStatusHype
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding—0
VeriGen: A Large Language Model for Verilog Code Generation—0
Unsupervised Adaptation for Statistical Machine Translation—0
Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization—0
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One—0
VERTa: a Linguistically-motivated Metric at the WMT15 Metrics Task—0
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training—0
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation—0
Which side are you on? Insider-Outsider classification in conspiracy-theoretic social media—0
VIANA: Visual Interactive Annotation of Argumentation—0
UNITER: Learning UNiversal Image-TExt Representations—0
Which techniques does your application use?: An information extraction framework for scientific articles—0
Which Words Matter in Defining Phrase Reordering Behavior in Statistical Machine Translation?—0
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors—0
ViDAS: Vision-based Danger Assessment and Scoring—0
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification—0
Word Sense Induction with Knowledge Distillation from BERT—0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training—0
Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction—0
Word Sketches for Turkish—0
Video Description: A Survey of Methods, Datasets and Evaluation Metrics—0
Video Emotion Open-vocabulary Recognition Based on Multimodal Large Language Model—0
Whisper-GPT: A Hybrid Representation Audio Large Language Model—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
Video Imprint—0
WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction—0
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets—0
Video Language Model Pretraining with Spatio-temporal Masking—0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
VideoLLM-online: Online Video Large Language Model for Streaming Video—0
Video LLMs for Temporal Reasoning in Long Videos—0
White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs—0
VideoOrion: Tokenizing Object Dynamics in Videos—0
VideoPoet: A Large Language Model for Zero-Shot Video Generation—0
Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis—0
Unsupervised Adverbial Identification in Modern Chinese Literature—0
Universal Conditional Masked Language Pre-training for Neural Machine Translation—0
Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
Unsupervised and Few-shot Parsing from Pretrained Language Models—0
VidLA: Video-Language Alignment at Scale—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection—0
Vietnamese Text Accent Restoration with Statistical Machine Translation—0
Who's Harry Potter? Approximate Unlearning in LLMs—0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization—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