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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Adapting Learned Sparse Retrieval for Long DocumentsCode0
Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing—0
Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model—0
Test-Time Training on Nearest Neighbors for Large Language ModelsCode1
Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy—0
Leveraging Training Data in Few-Shot Prompting for Numerical ReasoningCode0
PaLI-X: On Scaling up a Multilingual Vision and Language ModelCode1
Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews—0
Do Language Models Know When They're Hallucinating References?Code0
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User InstructionsCode1
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Image Captioning with Multi-Context Synthetic Data—0
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections—0
Large Language Models are not Fair EvaluatorsCode1
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image ClassificationCode1
Multiscale Positive-Unlabeled Detection of AI-Generated TextsCode2
A Quantitative Review on Language Model Efficiency Research—0
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales—0
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR—0
Rethinking Masked Language Modeling for Chinese Spelling CorrectionCode1
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model ApplicationCode2
Learning a Structural Causal Model for Intuition Reasoning in ConversationCode1
Generating EDU Extracts for Plan-Guided Summary Re-RankingCode0
FuseCap: Leveraging Large Language Models for Enriched Fused Image CaptionsCode1
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training—0
Augmenting Large Language Model Translators via Translation Memories—0
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Query-Efficient Black-Box Red Teaming via Bayesian OptimizationCode1
Matrix Information Theory for Self-Supervised LearningCode1
Language Models Can Improve Event Prediction by Few-Shot Abductive ReasoningCode2
Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning—0
Large language models improve Alzheimer's disease diagnosis using multi-modality data—0
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)—0
CONA: A novel CONtext-Aware instruction paradigm for communication using large language model—0
DataChat: Prototyping a Conversational Agent for Dataset Search and VisualizationCode0
Honey, I Shrunk the Language: Language Model Behavior at Reduced ScaleCode0
External Language Model Integration for Factorized Neural Transducers—0
From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models—0
Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across LanguagesCode0
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based RepresentationsCode1
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing—0
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry ClassificationCode0
Zero-shot Visual Question Answering with Language Model FeedbackCode0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
Backpack Language ModelsCode1
An Empirical Comparison of LM-based Question and Answer Generation Methods—0
An Investigation of Noise in Morphological InflectionCode0
Green Runner: A tool for efficient model selection from model repositories—0
Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model—0
Schema-Guided User Satisfaction Modeling for Task-Oriented DialoguesCode1
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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