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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15401–15450 of 17610 papers

TitleStatusHype
A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification—0
A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models—0
A Simple and Efficient Method To Generate Word Sense Representations—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension—0
A Simple Cache Model for Image Recognition—0
Accelerating Multilingual Language Model for Excessively Tokenized Languages—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
A Simple Language Model based on PMI Matrix Approximations—0
A Simple Model for Distantly Supervised Relation Extraction—0
A Simple, Yet Effective Approach to Finding Biases in Code Generation—0
A Simple yet Efficient Ensemble Approach for AI-generated Text Detection—0
Multiperiodic Processes: Ergodic Sources with a Sublinear Entropy—0
Ask Language Model to Clean Your Noisy Translation Data—0
"Ask Me Anything": How Comcast Uses LLMs to Assist Agents in Real Time—0
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios—0
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search—0
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
A Span Extraction Approach for Information Extraction on Visually-Rich Documents—0
Aspect-based Academic Search using Domain-specific KB—0
Aspect-Based Sentiment Analysis using BERT—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
Aspect Oriented Suggestion Extraction from Online Reviews—0
A Speed Odyssey for Deployable Quantization of LLMs—0
A spelling correction model for end-to-end speech recognition—0
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
A Split-and-Privatize Framework for Large Language Model Fine-Tuning—0
ASR4REAL: An extended benchmark for speech models—0
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
ASR for Documenting Acutely Under-Resourced Indigenous Languages—0
ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks—0
ASR Rescoring and Confidence Estimation with ELECTRA—0
Assamese-English Bilingual Machine Translation—0
Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering—0
Assessing and Understanding Creativity in Large Language Models—0
Assessing Discourse Relations in Language Generation from GPT-2—0
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning—0
Assessing GPT4-V on Structured Reasoning Tasks—0
Assessing Out-of-Domain Language Model Performance from Few Examples—0
Assessing Relative Sentence Complexity using an Incremental CCG Parser—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Assessing the Aesthetic Evaluation Capabilities of GPT-4 with Vision: Insights from Group and Individual Assessments—0
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation—0
Assessing the Effectiveness of GPT-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights—0
Assessing the efficacy of large language models in generating accurate teacher responses—0
Assessing the Impact of Prompting Methods on ChatGPT's Mathematical Capabilities—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