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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 66516700 of 17610 papers

TitleStatusHype
Regularizing Neural Networks by Penalizing Confident Output DistributionsCode0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model AlignmentCode0
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative TrainingCode0
Language Model Tokenizers Introduce Unfairness Between LanguagesCode0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Self-training with Two-phase Self-augmentation for Few-shot Dialogue GenerationCode0
Towards a Path Dependent Account of Category FluencyCode0
Selective Token Generation for Few-shot Natural Language GenerationCode0
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document UnderstandingCode0
OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion ParametersCode0
Selective Text Augmentation with Word Roles for Low-Resource Text ClassificationCode0
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and MappingCode0
Rethinking Complex Neural Network Architectures for Document ClassificationCode0
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing TasksCode0
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model TranslationsCode0
Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission PredictionCode0
Language Model Transformers as Evaluators for Open-domain DialoguesCode0
Selecting Large Language Model to Fine-tune via Rectified Scaling LawCode0
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language ModelsCode0
Online Normalization for Training Neural NetworksCode0
MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic ScenariosCode0
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model PromptingCode0
Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by BettingCode0
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue ModelCode0
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language ModelCode0
Iterative Pseudo-Labeling for Speech RecognitionCode0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
Surgical Feature-Space Decomposition of LLMs: Why, When and How?Code0
Online Back-Parsing for AMR-to-Text GenerationCode0
Segmenting Watermarked Texts From Language ModelsCode0
Multi-task Learning of Negation and Speculation for Targeted Sentiment ClassificationCode0
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment AnalysisCode0
LM-CORE: Language Models with Contextually Relevant External KnowledgeCode0
Layered Unlearning for Adversarial RelearningCode0
Llama Guard: LLM-based Input-Output Safeguard for Human-AI ConversationsCode0
Towards a text-based quantitative and explainable histopathology image analysisCode0
Towards a RAG-based Summarization Agent for the Electron-Ion ColliderCode0
Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First MeetingCode0
Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level TrainingCode0
Neural Authorship Attribution: Stylometric Analysis on Large Language ModelsCode0
Maybe Deep Neural Networks are the Best Choice for Modeling Source CodeCode0
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback SystemsCode0
On Extractive and Abstractive Neural Document Summarization with Transformer Language ModelsCode0
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQACode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize HierarchicallyCode0
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified