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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16501–16550 of 17610 papers

TitleStatusHype
Collaborative Cross-modal Fusion with Large Language Model for Recommendation—0
Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application -- the case of Tunisian Arabic and the Social Media—0
Collaborative Storytelling with Human Actors and AI Narrators—0
Collaborative Storytelling with Large-scale Neural Language Models—0
Collecting Code-Switched Data from Social Media—0
Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking—0
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code—0
CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction—0
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification—0
ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning—0
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French—0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining Constraint Programming Reasoning with Large Language Model Predictions—0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification—0
Combining Domain Adaptation Approaches for Medical Text Translation—0
Combining EBMT, SMT, TM and IR Technologies for Quality and Scale—0
Combining elicited imitation and fluency features for oral proficiency measurement—0
Combining Extraction and Generation for Constructing Belief-Consequence Causal Links—0
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge—0
Combining Language and Graph Models for Semi-structured Information Extraction on the Web—0
Post Training Quantization of Large Language Models with Microscaling Formats—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Combining Statistical Translation Techniques for Cross-Language Information Retrieval—0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition—0
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages—0
Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity—0
COMET: A Neural Framework for MT Evaluation—0
Comics for Everyone: Generating Accessible Text Descriptions for Comic Strips—0
Command A: An Enterprise-Ready Large Language Model—0
Commonsense Evidence Generation and Injection in Reading Comprehension—0
Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Communicating Activations Between Language Model Agents—0
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods—0
Communication-Efficient Agnostic Federated Averaging—0
Communication-Efficient Federated Distillation—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission—0
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo—0
Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees—0
Comparative Study of Language Models on Cross-Domain Data with Model Agnostic Explainability—0
Comparing CRF and template-matching in phrasing tasks within a Hybrid MT system—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance—0
Comparing Generalization in Learning with Limited Numbers of Exemplars: Transformer vs. RNN in Attractor Dynamics—0
Comparing Generative Chatbots Based on Process Requirements—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