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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16751–16800 of 17610 papers

TitleStatusHype
Controlling for Stereotypes in Multimodal Language Model Evaluation—0
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status—0
Controlling Large Language Model Agents with Entropic Activation Steering—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Controlling Linguistic Style Aspects in Neural Language Generation—0
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms—0
Controlling Translation Formality Using Pre-trained Multilingual Language Models—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
Control Search Rankings, Control the World: What is a Good Search Engine?—0
Conversational Alignment with Artificial Intelligence in Context—0
Conversational Ontology Alignment with ChatGPT—0
Conversational Query Reformulation with the Guidance of Retrieved Documents—0
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph—0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition—0
Conversational Speech Recognition Needs Data? Experiments with Austrian German—0
Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models—0
Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations—0
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues—0
Conversion between Scripts of Punjabi: Beyond Simple Transliteration—0
Converting Continuous-Space Language Models into N-Gram Language Models for Statistical Machine Translation—0
Convert Language Model into a Value-based Strategic Planner—0
ConVEx: Data-Efficient and Few-Shot Slot Labeling—0
Convolutional Neural Networks for Authorship Attribution of Short Texts—0
Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction—0
Convolutional Sequence Modeling Revisited—0
Convolutions Are All You Need (For Classifying Character Sequences)—0
ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations—0
Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code)—0
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions—0
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction—0
CORD19STS: COVID-19 Semantic Textual Similarity Dataset—0
Core Context Aware Attention for Long Context Language Modeling—0
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments—0
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering—0
Coreference Resolution through a seq2seq Transition-Based System—0
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation—0
CoreLM: Coreference-aware Language Model Fine-Tuning—0
Corporate Bankruptcy Prediction with BERT Model—0
Corporate Bankruptcy Prediction with Domain-Adapted BERT—0
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation—0
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models—0
Correcting Automated and Manual Speech Transcription Errors using Warped Language Models—0
Correcting Large Language Model Behavior via Influence Function—0
Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages—0
Correcting Serial Grammatical Errors based on N-grams and Syntax—0
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization—0
Correction Focused Language Model Training for Speech Recognition—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence 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