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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15011550 of 17610 papers

TitleStatusHype
FANformer: Improving Large Language Models Through Effective Periodicity ModelingCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
Generative Spoken Language Modeling from Raw AudioCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
Generative News RecommendationCode1
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel ProteinsCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Generative Multimodal Entity LinkingCode1
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game ModelsCode1
Counterfactual Token Generation in Large Language ModelsCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language ModelCode1
Generator-Retriever-Generator Approach for Open-Domain Question AnsweringCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast AdaptationCode1
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct DecodingCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
Adaptive Input Representations for Neural Language ModelingCode1
cosFormer: Rethinking Softmax in AttentionCode1
Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0Code1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
Interesting Scientific Idea Generation using Knowledge Graphs and LLMs: Evaluations with 100 Research Group LeadersCode1
Generating Sequences With Recurrent Neural NetworksCode1
Instruction Following without Instruction TuningCode1
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS DecodingCode1
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLMCode1
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-trainingCode1
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Copy Is All You NeedCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
Generating Label Cohesive and Well-Formed Adversarial ClaimsCode1
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model HackathonCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Generating Sentences from a Continuous SpaceCode1
Generative Entity Typing with Curriculum LearningCode1
Adaptive Computation Time for Recurrent Neural NetworksCode1
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
General Preference Modeling with Preference Representations for Aligning Language ModelsCode1
Generated Knowledge Prompting for Commonsense ReasoningCode1
Generalization through Memorization: Nearest Neighbor Language ModelsCode1
Adaptive Attention Span in TransformersCode1
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue GenerationCode1
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
Adaptive Attention Span in Computer VisionCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM AgentsCode1
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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