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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10351–10400 of 17610 papers

TitleStatusHype
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents—0
POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model—0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task—0
Postech's System Description for Medical Text Translation Task—0
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference—0
Posterior Sampling via Autoregressive Generation—0
Potential of large language model-powered nudges for promoting daily water and energy conservation—0
Potential Renovation of Information Search Process with the Power of Large Language Model for Healthcare—0
PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks—0
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding—0
PPLM Revisited: Steering and Beaming a Lumbering Mammoth to Control Text Generation—0
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries—0
Pragmatic Neural Language Modelling in Machine Translation—0
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation—0
PRE: A Peer Review Based Large Language Model Evaluator—0
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models—0
Precision Highway for Ultra Low-Precision Quantization—0
Pre-Computable Multi-Layer Neural Network Language Models—0
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3—0
Predictability and Causality in Spanish and English Natural Language Generation—0
Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining—0
Predicting Affective States from Screen Text Sentiment—0
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model—0
Predicting Anti-microbial Resistance using Large Language Models—0
Predicting Attention Sparsity in Transformers—0
Predicting Attention Sparsity in Transformers—0
Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approaches—0
Predicting Distance matrix with large language models—0
Predicting Grammaticality on an Ordinal Scale—0
Predicting human similarity judgments with distributional models: The value of word associations.—0
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation—0
Predicting Machine Translation Adequacy with Document Embeddings—0
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity—0
Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals—0
Predicting Prepositions for SMT—0
Predicting Pronouns across Languages with Continuous Word Spaces—0
Predicting Pronouns with a Convolutional Network and an N-gram Model—0
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency—0
Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models—0
Predicting sense convergence with distributional semantics: an application to the CogaLex 2014 shared task—0
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models—0
Predicting the Ordering of Characters in Japanese Historical Documents—0
Predictions For Pre-training Language Models—0
Predictive Incremental Parsing Helps Language Modeling—0
Predictive power of word surprisal for reading times is a linear function of language model quality—0
Predictive Representation Learning for Language Modeling—0
Predictive text for agglutinative and polysynthetic languages—0
Predictive Text for Agglutinative and Polysynthetic Languages—0
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning—0
Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation—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