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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12051–12100 of 17610 papers

TitleStatusHype
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models—0
Unsupervised Paraphrasability Prediction for Compound Nominalizations—0
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—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
Zero-shot cross-lingual Meaning Representation Transfer: Annotation of Hungarian using the Prague Functional Generative Description—0
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis—0
Unsupervised Neural Machine Translation with Generative Language Models Only—0
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study—0
What do RNN Language Models Learn about Filler--Gap Dependencies?—0
Understanding Language Model Circuits through Knowledge Editing—0
Unsupervised neural and Bayesian models for zero-resource speech processing—0
What do we need to know about an unknown word when parsing German—0
Unsupervised Natural Question Answering with a Small Model—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
Unsupervised Multi-View Post-OCR Error Correction With Language Models—0
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition—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
Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—0
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages—0
Zero-shot cross-lingual transfer in instruction tuning of large language models—0
Unsupervised Morphology-Based Vocabulary Expansion—0
ZiGong 1.0: A Large Language Model for Financial Credit—0
Unsupervised Morphological Tree Tokenizer—0
What represents ``style'' in authorship attribution?—0
What Should Baby Models Read? Exploring Sample-Efficient Data Composition on Model Performance—0
What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval—0
What's in a Name? Beyond Class Indices for Image Recognition—0
Unified Text Structuralization with Instruction-tuned Language Models—0
What's in your Head? Emergent Behaviour in Multi-Task Transformer Models—0
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models—0
Adapting Long Context NLM for ASR Rescoring in Conversational Agents—0
ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
What Syntactic Structures block Dependencies in RNN Language Models?—0
What the [MASK]? Making Sense of Language-Specific BERT Models—0
Unsupervised Melody Segmentation Based on a Nested Pitman-Yor Language Model—0
Unsupervised Learning on an Approximate Corpus—0
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation—0
Knowledge Injection into Dialogue Generation via Language Models—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