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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14511500 of 17610 papers

TitleStatusHype
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation ModelsCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
Generative Entity Typing with Curriculum LearningCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
Counterfactual Token Generation in Large Language ModelsCode1
AdaSplash: Adaptive Sparse Flash AttentionCode1
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLMCode1
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS DecodingCode1
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from TextCode1
Interesting Scientific Idea Generation using Knowledge Graphs and LLMs: Evaluations with 100 Research Group LeadersCode1
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct DecodingCode1
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue SystemsCode1
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot ClassificationCode1
Generating Sentences from a Continuous SpaceCode1
Protein Structure Tokenization: Benchmarking and New RecipeCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Generating Sequences With Recurrent Neural NetworksCode1
Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0Code1
Generate to Understand for RepresentationCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
cosFormer: Rethinking Softmax in AttentionCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
Mask-Predict: Parallel Decoding of Conditional Masked Language ModelsCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
Generating Label Cohesive and Well-Formed Adversarial ClaimsCode1
Generative Prompt Tuning for Relation ClassificationCode1
Grounded Multi-Hop VideoQA in Long-Form Egocentric VideosCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
Copy Is All You NeedCode1
Generalization through Memorization: Nearest Neighbor Language ModelsCode1
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue GenerationCode1
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic InteractionCode1
General Preference Modeling with Preference Representations for Aligning Language ModelsCode1
GCoder: Improving Large Language Model for Generalized Graph Problem SolvingCode1
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human AttentionCode1
Gemstones: A Model Suite for Multi-Faceted Scaling LawsCode1
Control Prefixes for Parameter-Efficient Text GenerationCode1
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Gated Linear Attention Transformers with Hardware-Efficient TrainingCode1
GateLoop: Fully Data-Controlled Linear Recurrence for Sequence ModelingCode1
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree SearchCode1
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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