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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12001–12050 of 17610 papers

TitleStatusHype
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching—0
Weakly Supervised Dense Video Captioning—0
Unsupervised Semantic Role Induction with Global Role Ordering—0
Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors—0
Weakly supervised information extraction from inscrutable handwritten document images—0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning—0
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion—0
UniCoder: Scaling Code Large Language Model via Universal Code—0
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion—0
Wearable intelligent throat enables natural speech in stroke patients with dysarthria—0
Zero-resource Speech Translation and Recognition with LLMs—0
Unsupervised Pretraining for Sequence to Sequence Learning—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT—0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation—0
Weblio Pre-reordering Statistical Machine Translation System—0
WebMap -- Large Language Model-assisted Semantic Link Induction in the Web—0
Zero-shot Action Localization via the Confidence of Large Vision-Language Models—0
Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track—0
Weight decay induces low-rank attention layers—0
Weighted-Entropy-Based Quantization for Deep Neural Networks—0
WeightedKV: Attention Scores Weighted Key-Value Cache Merging for Large Language Models—0
Weighted Sampling for Masked Language Modeling—0
Weight Prediction Boosts the Convergence of AdamW—0
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models—0
Unsupervised Prediction of Acceptability Judgements—0
Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM—0
WeLM: A Well-Read Pre-trained Language Model for Chinese—0
Unsupervised Paraphrasing with Pretrained Language Models—0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
WenyanGPT: A Large Language Model for Classical Chinese Tasks—0
WEPO: Web Element Preference Optimization for LLM-based Web Navigation—0
Uniform Masking Prevails in Vision-Language Pretraining—0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
West-of-N: Synthetic Preferences for Self-Improving Reward Models—0
Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval—0
WFST-Based Grapheme-to-Phoneme Conversion: Open Source tools for Alignment, Model-Building and Decoding—0
Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs—0
What are human values, and how do we align AI to them?—0
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis—0
What are the limitations on the flux of syntactic dependencies? Evidence from UD treebanks—0
What are the limits of cross-lingual dense passage retrieval for low-resource languages?—0
What Are Tools Anyway? A Survey from the Language Model Perspective—0
What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysis—0
What BERT Based Language Model Learns in Spoken Transcripts: An Empirical Study—0
Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge—0
What Can a Generative Language Model Answer About a Passage?—0
What can we gain from language models for morphological inflection?—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