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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5851–5900 of 17610 papers

TitleStatusHype
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition—0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
A Methodology for Obtaining Concept Graphs from Word Graphs—0
A Method on Searching Better Activation Functions—0
Amharic-English Speech Translation in Tourism Domain—0
Amharic Word Sequence Prediction—0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
A Mixture of h-1 Heads is Better than h Heads—0
A Mixture of h - 1 Heads is Better than h Heads—0
A ML-LLM pairing for better code comment classification—0
Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers—0
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
A more abstractive summarization model—0
AMPO: Active Multi-Preference Optimization—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
A Multi-Expert Large Language Model Architecture for Verilog Code Generation—0
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation—0
A Multi-Granularity Retrieval Framework for Visually-Rich Documents—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models—0
A Multimodal Automated Interpretability Agent—0
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study—0
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction—0
A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling—0
A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation—0
A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—0
An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model—0
An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection—0
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design—0
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models—0
Analyse des performances de mod\`eles de langage sub-lexicale pour des langues peu-dot\'ees \`a morphologie riche (Performance analysis of sub-word language modeling for under-resourced languages with rich morphology: case study on Swahili and Amharic) [in French]—0
Analysing Dropout and Compounding Errors in Neural Language Models—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Analysing the Effect of Out-of-Domain Data on SMT Systems—0
Analysis of Argument Structure Constructions in the Large Language Model BERT—0
Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model—0
An Analysis of Semantically-Aligned Speech-Text Embeddings—0
Analysis of Plan-based Retrieval for Grounded Text Generation—0
Analysis of the User Perception of Chatbots in Education Using A Partial Least Squares Structural Equation Modeling Approach—0
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction—0
Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model—0
Analyzing and Mitigating Inconsistency in Discrete Audio Tokens for Neural Codec Language Models—0
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast—0
Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training—0
Analyzing FOMC Minutes: Accuracy and Constraints of Language Models—0
以語言模型判斷學習者文句流暢度(Analyzing Learners `Writing Fluency Based on Language Model)[In Chinese]—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