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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 20512100 of 17610 papers

TitleStatusHype
CTRL: A Conditional Transformer Language Model for Controllable GenerationCode1
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language TechnologiesCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement LearningCode1
Working Memory Capacity of ChatGPT: An Empirical StudyCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
Imposing Relation Structure in Language-Model Embeddings Using Contrastive LearningCode1
Improved training of end-to-end attention models for speech recognitionCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Implementing contextual biasing in GPU decoder for online ASRCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense RetrievalCode1
Implicit Language Models are RNNs: Balancing Parallelization and ExpressivityCode1
ImProver: Agent-Based Automated Proof OptimizationCode1
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation LearningCode1
IFSeg: Image-free Semantic Segmentation via Vision-Language ModelCode1
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric KnowledgeCode1
Image Hijacks: Adversarial Images can Control Generative Models at RuntimeCode1
Identifying Interpretable Subspaces in Image RepresentationsCode1
A Better Way to Do Masked Language Model ScoringCode1
Identifying the Risks of LM Agents with an LM-Emulated SandboxCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language ModelCode1
If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept DescriptionsCode1
Image Super-Resolution with Text Prompt DiffusionCode1
CDLM: Cross-Document Language ModelingCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
iBOT: Image BERT Pre-Training with Online TokenizerCode1
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language ModelsCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
A Language Model based Framework for New Concept Placement in OntologiesCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal CapabilitiesCode1
IDAS: Intent Discovery with Abstractive SummarizationCode1
Image-Text Co-Decomposition for Text-Supervised Semantic SegmentationCode1
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed HypergraphsCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video UnderstandingCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation PerturbationCode1
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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