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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 65516600 of 17610 papers

TitleStatusHype
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning0
OpenGraph: Towards Open Graph Foundation ModelsCode3
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks0
Towards Accurate Lip-to-Speech Synthesis in-the-Wild0
Chaining thoughts and LLMs to learn DNA structural biophysicsCode0
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code0
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens IntactCode3
NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free AttentionCode1
DMoERM: Recipes of Mixture-of-Experts for Effective Reward ModelingCode1
LAB: Large-Scale Alignment for ChatBotsCode5
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)0
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs0
SoftTiger: A Clinical Foundation Model for Healthcare WorkflowsCode7
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation TrainingCode0
Enhancing Jailbreak Attacks with Diversity Guidance0
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-CommerceCode1
Merging Text Transformer Models from Different InitializationsCode1
Leveraging pre-trained language models for code generationCode0
Resonance RoPE: Improving Context Length Generalization of Large Language ModelsCode1
FAC^2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition0
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction0
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction TasksCode3
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient TuningCode1
TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model EncodingsCode1
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model0
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models0
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval0
FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient FinetuningCode5
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?0
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language ModelsCode7
PaECTER: Patent-level Representation Learning using Citation-informed Transformers0
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration0
VIXEN: Visual Text Comparison Network for Image Difference CaptioningCode0
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic InteractionCode1
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-TrainingCode2
Merino: Entropy-driven Design for Generative Language Models on IoT Devices0
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners0
Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem0
Data Interpreter: An LLM Agent For Data Science0
Multi-objective Differentiable Neural Architecture SearchCode1
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language ModelCode1
Grounding Language Models for Visual Entity RecognitionCode1
Trends, Applications, and Challenges in Human Attention ModellingCode2
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling0
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic DimensionCode1
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
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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