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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 6951–7000 of 17610 papers

TitleStatusHype
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation—0
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study—0
Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningCode1
On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model InferenceCode2
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education—0
Self-consistent context aware conformer transducer for speech recognition—0
ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and RefinementCode3
The Quantified Boolean Bayesian Network: Theory and Experiments with a Logical Graphical ModelCode1
A self-supervised framework for learning whole slide representations—0
GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data—0
Understanding the Weakness of Large Language Model Agents within a Complex Android EnvironmentCode1
G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in GermanCode0
Large Language Models for Captioning and Retrieving Remote Sensing Images—0
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning—0
Neural Models for Source Code Synthesis and Completion—0
Integrating LLMs for Explainable Fault Diagnosis in Complex Systems—0
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive SurveyCode3
Question Aware Vision Transformer for Multimodal Reasoning—0
How do Transformers perform In-Context Autoregressive Learning?—0
Noise Contrastive Alignment of Language Models with Explicit RewardsCode3
Text-to-Code Generation with Modality-relative Pre-training—0
Improving Agent Interactions in Virtual Environments with Language Models—0
CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Spirit LM: Interleaved Spoken and Written Language ModelCode4
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-AgentsCode3
Decoupled Sequence and Structure Generation for Realistic Antibody DesignCode0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language ModelsCode7
Driving Everywhere with Large Language Model Policy Adaptation—0
An Interactive Agent Foundation Model—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
A Masked language model for multi-source EHR trajectories contextual representation learning—0
Structure-Informed Protein Language ModelCode1
ApiQ: Finetuning of 2-Bit Quantized Large Language ModelCode1
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning—0
Leveraging LLMs for Unsupervised Dense Retriever RankingCode0
Language-Based Augmentation to Address Shortcut Learning in Object Goal NavigationCode0
CodeIt: Self-Improving Language Models with Prioritized Hindsight ReplayCode1
SumRec: A Framework for Recommendation using Open-Domain DialogueCode0
StableMask: Refining Causal Masking in Decoder-only Transformer—0
ScreenAI: A Vision-Language Model for UI and Infographics UnderstandingCode2
Can Large Language Model Agents Simulate Human Trust Behavior?Code2
Direct Language Model Alignment from Online AI Feedback—0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
Personalized Language Modeling from Personalized Human FeedbackCode1
Sentiment-enhanced Graph-based Sarcasm Explanation in DialogueCode0
Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience—0
Harnessing the Plug-and-Play Controller by Prompting—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified