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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13011350 of 17610 papers

TitleStatusHype
Frequency Autoregressive Image Generation with Continuous Tokens0
Is Your Video Language Model a Reliable Judge?0
R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement LearningCode5
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information RetrievalCode2
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization0
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining0
LLM-based Iterative Approach to Metamodeling in Automotive0
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons0
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation0
Look Before You Leap: Using Serialized State Machine for Language Conditioned Robotic Manipulation0
PromptPex: Automatic Test Generation for Language Model PromptsCode2
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation0
Generalized Interpolating Discrete DiffusionCode2
Wanda++: Pruning Large Language Models via Regional GradientsCode0
Better Process Supervision with Bi-directional Rewarding Signals0
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach0
TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reduction0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks0
Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining0
KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney DiseaseCode0
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management0
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMCode2
Measuring temporal effects of agent knowledge by date-controlled tool use0
Scaling Rich Style-Prompted Text-to-Speech DatasetsCode2
Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking CapabilitiesCode2
The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy0
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services0
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities0
From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design0
L^2M: Mutual Information Scaling Law for Long-Context Language ModelingCode1
Know Thy Judge: On the Robustness Meta-Evaluation of LLM Safety Judges0
An Egocentric Vision-Language Model based Portable Real-time Smart AssistantCode2
Human Preferences for Constructive Interactions in Language Model Alignment0
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design DecisionsCode0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues0
Collaborative Expert LLMs Guided Multi-Objective Molecular OptimizationCode2
Parallelized Planning-Acting for Efficient LLM-based Multi-Agent SystemsCode3
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability0
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary AlgorithmsCode0
Tabby: Tabular Data Synthesis with Language Models0
Hierarchical Re-ranker Retriever (HRR)0
Vision-Language Model IP Protection via Prompt-based Learning0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models0
A Phylogenetic Approach to Genomic Language ModelingCode3
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks0
Trust, Experience, and Innovation: Key Factors Shaping American Attitudes About AI0
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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