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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 34013450 of 17610 papers

TitleStatusHype
Conformal Language ModelingCode1
Improving Transformer Optimization Through Better InitializationCode1
Log Parsing: How Far Can ChatGPT Go?Code1
LitCab: Lightweight Language Model Calibration over Short- and Long-form ResponsesCode1
Lipschitz Recurrent Neural NetworksCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
CTRL: A Conditional Transformer Language Model for Controllable GenerationCode1
Scene Transformer: A unified architecture for predicting multiple agent trajectoriesCode1
Connecting Language and Vision for Natural Language-Based Vehicle RetrievalCode1
In-context Autoencoder for Context Compression in a Large Language ModelCode1
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification TaskCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question AnsweringCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
LION : Empowering Multimodal Large Language Model with Dual-Level Visual KnowledgeCode1
SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific ResearchCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
Incorporating Large Language Models into Production Systems for Enhanced Task Automation and FlexibilityCode1
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human AnnotationCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
LinkTransformer: A Unified Package for Record Linkage with Transformer Language ModelsCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
AuditWen:An Open-Source Large Language Model for AuditCode1
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERTCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-AnsweringCode1
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support ConversationCode1
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary InitializationCode1
LITE: Modeling Environmental Ecosystems with Multimodal Large Language ModelsCode1
Gradient-Based Constrained Sampling from Language ModelsCode1
Linear Transformers Are Secretly Fast Weight ProgrammersCode1
Secure Distributed Training at ScaleCode1
Adaptive Input Representations for Neural Language ModelingCode1
INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language ModelCode1
Linear Recurrent Units for Sequential RecommendationCode1
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningCode1
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMsCode1
Likelihood-Based Diffusion Language ModelsCode1
Linearly Mapping from Image to Text SpaceCode1
Segatron: Segment-Aware Transformer for Language Modeling and UnderstandingCode1
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast AdaptationCode1
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
InfoLM: A New Metric to Evaluate Summarization & Data2Text GenerationCode1
Linformer: Self-Attention with Linear ComplexityCode1
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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