LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

arXiv:2609.33521 · cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/Jieerbobo/LLM4Trust

Terminology

• Accuracy — 59× in this paper · explained in 1 episode
• GNNs — 19× in this paper · explained in 2 episodes
• Benchmark — 17× in this paper · explained in 2 episodes
• alpha — 17× in this paper · explained in 1 episode
• Robustness — 14× in this paper · explained in 5 episodes
• Explainability — 11× in this paper · explained in 3 episodes
• Aggregation — 9× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• IEEE Symposium on Security and Privacy — 5× in this paper
• Machine Learning — 5× in this paper
• Benchmarking — 4× in this paper · explained in 3 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Anomaly Detection — 3× in this paper
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Forest Fire — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Network and Distributed System Security — 2× in this paper
• Abstraction — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• F1-macro and Balanced Accuracy — 1× in this paper
• F1-micro and Mean Absolute Error — 1× in this paper
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Stochastic Block — 1× in this paper

Sources

• AD-LLM: Benchmarking Large Language Models for Anomaly Detection
• DeepSeek-V3 Technical Report
• Qwen2.5 Technical Report
• SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
• An Evaluation of GPT-4 on the ETHICS Dataset
• LawBench: Benchmarking Legal Knowledge of Large Language Models
• Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
• GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments

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