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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9251–9300 of 17610 papers

TitleStatusHype
Learning to Scale Multilingual Representations for Vision-Language Tasks—0
Learning to Select: A Fully Attentive Approach for Novel Object Captioning—0
Learning to Select In-Context Demonstration Preferred by Large Language Model—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Learning to Skip for Language Modeling—0
Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions—0
Learning to translate from graded and negative relevance information—0
Learning to Write by Learning the Objective—0
Learning to Write Notes in Electronic Health Records—0
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System—0
Learning Visual Grounding from Generative Vision and Language Model—0
Learning Visually Grounded Sentence Representations—0
Learning Visual N-Grams from Web Data—0
Learning Visual Representations with Caption Annotations—0
Learning with Noise-Contrastive Estimation: Easing training by learning to scale—0
Learning Word Meanings and Grammar for Describing Everyday Activities in Smart Environments—0
Learning Word Representations with Regularization from Prior Knowledge—0
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition—0
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling—0
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages—0
Learn Your Reference Model for Real Good Alignment—0
LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT—0
LEDD: Large Language Model-Empowered Data Discovery in Data Lakes—0
Left-to-Right Tree-to-String Decoding with Prediction—0
Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments—0
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval—0
LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training—0
Legal Prompt Engineering for Multilingual Legal Judgement Prediction—0
Legal Prompting: Teaching a Language Model to Think Like a Lawyer—0
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension—0
Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model—0
LEGO: Language Model Building Blocks—0
LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
Lending Interaction Wings to Recommender Systems with Conversational Agents—0
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing—0
Less is More: A Closer Look at Semantic-based Few-Shot Learning—0
Less is More for Improving Automatic Evaluation of Factual Consistency—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement—0
Lessons from the Trenches on Reproducible Evaluation of Language Models—0
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish—0
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers—0
Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning—0
Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction—0
Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction—0
LETS-C: Leveraging Text Embedding for Time Series Classification—0
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers—0
Let Segment Anything Help Image Dehaze—0
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large 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