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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11251–11300 of 17610 papers

TitleStatusHype
Graph-Level Embedding for Time-Evolving Graphs—0
CapText: Large Language Model-based Caption Generation From Image Context and Description—0
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formulaCode0
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning—0
How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics—0
Exposing Attention Glitches with Flip-Flop Language Modeling—0
An Invariant Learning Characterization of Controlled Text GenerationCode0
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline—0
Adverbs, Surprisingly—0
Catalysis distillation neural network for the few shot open catalyst challenge—0
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment—0
Human or Not? A Gamified Approach to the Turing Test—0
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of MindCode0
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model PromptingCode0
Neuron to Graph: Interpreting Language Model Neurons at ScaleCode0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
KEYword based Sampling (KEYS) for Large Language Models—0
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation—0
Universality and Limitations of Prompt Tuning—0
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic StructuresCode0
GPT4GEO: How a Language Model Sees the World's Geography—0
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuningCode0
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Adapting Learned Sparse Retrieval for Long DocumentsCode0
Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing—0
Do Language Models Know When They're Hallucinating References?Code0
Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy—0
Image Captioning with Multi-Context Synthetic Data—0
Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model—0
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections—0
Leveraging Training Data in Few-Shot Prompting for Numerical ReasoningCode0
Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews—0
A Quantitative Review on Language Model Efficiency Research—0
Generating EDU Extracts for Plan-Guided Summary Re-RankingCode0
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales—0
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR—0
Augmenting Large Language Model Translators via Translation Memories—0
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training—0
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
Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning—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
External Language Model Integration for Factorized Neural Transducers—0
An Investigation of Noise in Morphological InflectionCode0
Honey, I Shrunk the Language: Language Model Behavior at Reduced ScaleCode0
An Empirical Comparison of LM-based Question and Answer Generation Methods—0
Emergent Agentic Transformer from Chain of Hindsight Experience—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