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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9651–9700 of 17610 papers

TitleStatusHype
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models—0
XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference—0
WeLM: A Well-Read Pre-trained Language Model for Chinese—0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding—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
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding—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
Unifying Multitrack Music Arrangement via Reconstruction Fine-Tuning and Efficient Tokenization—0
WFST-Based Grapheme-to-Phoneme Conversion: Open Source tools for Alignment, Model-Building and Decoding—0
Unlocking Spatial Comprehension in Text-to-Image Diffusion Models—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
Word-based Domain Adaptation for Neural Machine Translation—0
What Can a Generative Language Model Answer About a Passage?—0
What can we gain from language models for morphological inflection?—0
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models—0
Word Class Based Language Modeling: A Case of Upper Sorbian—0
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?—0
Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data—0
What Does BERT with Vision Look At?—0
What Does it Mean for a Language Model to Preserve Privacy?—0
What do Language Model Probabilities Represent? From Distribution Estimation to Response Prediction—0
What do Language Representations Really Represent?—0
Unlocking the Potential of Model Merging for Low-Resource Languages—0
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages—0
What do RNN Language Models Learn about Filler--Gap Dependencies?—0
Understanding Language Model Circuits through Knowledge Editing—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
What do we need to know about an unknown word when parsing German—0
Worldwide Federated Training of Language Models—0
What goes into a word: generating image descriptions with top-down spatial knowledge—0
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models—0
WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language Models—0
What is not where: the challenge of integrating spatial representations into deep learning architectures—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection—0
What Kind of Language Is Hard to Language-Model?—0
What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling—0
Word Importance Explains How Prompts Affect Language Model Outputs—0
Unsupervised Discovery of Unaccusative and Unergative Verbs—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