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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8901–8950 of 17610 papers

TitleStatusHype
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
Error-Correcting Codes For Approximate Neural Sequence Prediction—0
Error-Correcting Neural Sequence Prediction—0
Error Correction Environment for the Polish Parliamentary Corpus—0
Error Detection in Automatic Speech Recognition—0
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models—0
Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing—0
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization—0
Escaping Collapse: The Strength of Weak Data for Large Language Model Training—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
esCorpius: A Massive Spanish Crawling Corpus—0
ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices—0
ESG Sentiment Analysis: comparing human and language model performance including GPT—0
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining—0
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity—0
ESPnet-SpeechLM: An Open Speech Language Model Toolkit—0
Espresso: High Compression For Rich Extraction From Videos for Your Vision-Language Model—0
Establishing Task Scaling Laws via Compute-Efficient Model Ladders—0
Estimating Contribution Quality in Online Deliberations Using a Large Language Model—0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models—0
Estimating Numbers without Regression—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—0
Estimating related words computationally using language model from the Mahabharata - an Indian epic—0
Estimating senses with sets of lexically related words for Polish word sense disambiguation—0
Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation—0
Estimating the Causal Effects of Natural Logic Features in Neural NLI Models—0
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models—0
Estimating the Probability of Sampling a Trained Neural Network at Random—0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks—0
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
Ethics, Rules of Engagement, and AI: Neural Narrative Mapping Using Large Transformer Language Models—0
ETimeline: An Extensive Timeline Generation Dataset based on Large Language Model—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
EuroLLM-9B: Technical Report—0
EVA: An Embodied World Model for Future Video Anticipation—0
Evade ChatGPT Detectors via A Single Space—0
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria—0
Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning—0
Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing—0
Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction—0
Evaluating and Mitigating Discrimination in Language Model Decisions—0
Evaluating Apple Intelligence's Writing Tools for Privacy Against Large Language Model-Based Inference Attacks: Insights from Early Datasets—0
Evaluating Approaches to Personalizing Language Models—0
Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems—0
Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models—0
Evaluating ChatGPT text-mining of clinical records for obesity monitoring—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