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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 30513100 of 17610 papers

TitleStatusHype
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
Collective Constitutional AI: Aligning a Language Model with Public InputCode1
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image SequencesCode1
MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer LearningCode1
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein UnderstandingCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
Endowing Protein Language Models with Structural KnowledgeCode1
Memory Sharing for Large Language Model based AgentsCode1
Content-Based Collaborative Generation for Recommender SystemsCode1
MERMAID: Metaphor Generation with Symbolism and Discriminative DecodingCode1
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsCode1
Clover: Towards A Unified Video-Language Alignment and Fusion ModelCode1
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language ModelsCode1
Context-aware Decoding Reduces Hallucination in Query-focused SummarizationCode1
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction TasksCode1
MetaLA: Unified Optimal Linear Approximation to Softmax Attention MapCode1
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!Code1
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
Enabling Language Models to Fill in the BlanksCode1
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error CorrectionCode1
Empowering Large Language Model for Continual Video Question Answering with Collaborative PromptingCode1
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language ModelsCode1
Empower Entity Set Expansion via Language Model ProbingCode1
Empowering Large Language Model Agents through Action LearningCode1
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question AnsweringCode1
Enhancing Biomedical Relation Extraction with DirectionalityCode1
EMMA: Efficient Visual Alignment in Multi-Modal LLMsCode1
eMLM: A New Pre-training Objective for Emotion Related TasksCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
Emergent Analogical Reasoning in Large Language ModelsCode1
Emergence of Social Norms in Generative Agent Societies: Principles and ArchitectureCode1
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language ModelsCode1
EMO: Earth Mover Distance Optimization for Auto-Regressive Language ModelingCode1
A Study of Generative Large Language Model for Medical Research and HealthcareCode1
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-CommerceCode1
Misinfo Reaction Frames: Reasoning about Readers' Reactions to News HeadlinesCode1
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text GenerationCode1
Augmenting Interpretable Models with LLMs during TrainingCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
EmojiLM: Modeling the New Emoji LanguageCode1
Bioformer: an efficient transformer language model for biomedical text miningCode1
BioELECTRA:Pretrained Biomedical text Encoder using DiscriminatorsCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
CoLLM: A Large Language Model for Composed Image RetrievalCode1
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
MM-Instruct: Generated Visual Instructions for Large Multimodal Model AlignmentCode1
Elephants Never Forget: Testing Language Models for Memorization of Tabular DataCode1
BioBART: Pretraining and Evaluation of A Biomedical Generative Language ModelCode1
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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