SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15651–15700 of 17610 papers

TitleStatusHype
Automatically Detecting Online Deceptive Patterns in Real-time—0
Automatically Generating Rhythmic Verse with Neural Networks—0
Automatically Generating Rules of Malicious Software Packages via Large Language Model—0
Automatic Argument Quality Assessment -- New Datasets and Methods—0
Automatic Argument Quality Assessment - New Datasets and Methods—0
Automatic Assessment of Divergent Thinking in Chinese Language with TransDis: A Transformer-Based Language Model Approach—0
Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics—0
Automatic Assistance for Academic Word Usage—0
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study—0
Pareto Optimal Learning for Estimating Large Language Model Errors—0
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Automatic Conditional Generation of Personalized Social Media Short Texts—0
Automatic Construction of Discourse Corpora for Dialogue Translation—0
Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents—0
Automatic conversion of colloquial Finnishto standard Finnish—0
Automatic Correction of Arabic Text: a Cascaded Approach—0
Automatic Data Expansion for Customer-care Spoken Language Understanding—0
Automatic Demonstration Selection for LLM-based Tabular Data Classification—0
Automatic Detection of Borrowings in Low-Resource Languages of the Caucasus: Andic branch—0
Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies—0
Automatic Dialect Density Estimation for African American English—0
Automatic Documentation of ICD Codes with Far-Field Speech Recognition—0
Automatic Extraction of Personality from Text: Challenges and Opportunities—0
Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature—0
Automatic Feature Learning for Essence: a Case Study on Car Sequencing—0
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models—0
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks—0
Automatic High-quality Verilog Assertion Generation through Subtask-Focused Fine-Tuned LLMs and Iterative Prompting—0
Automatic Identification of Arabic Language Varieties and Dialects in Social Media—0
Automatic Identification of Rhetorical Questions—0
Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models—0
Automatic Item Generation for Personality Situational Judgment Tests with Large Language Models—0
Automatic Knowledge Augmentation for Generative Commonsense Reasoning—0
Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish—0
Automatic Learning of Subword Dependent Model Scales—0
Automatic Long Audio Alignment and Confidence Scoring for Conversational Arabic Speech—0
Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model—0
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification—0
Automatic Myanmar Image Captioning using CNN and LSTM-Based Language Model—0
Automatic Nominalization of Clauses—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
Automatic Poetry Generation from Prosaic Text—0
Automatic Post-Editing for the DiscoMT Pronoun Translation Task—0
Automatic Proficiency Assessment in L2 English Learners—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Learning to Rewrite Prompts for Personalized Text Generation—0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System—0
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model—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