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Found 14 papers, 9 papers with code
MELON: Indirect Prompt Injection Defense via Masked Re-execution and Tool Comparison
Moreover, we show that combining MELON with a SOTA prompt augmentation defense (denoted as MELON-Aug) further improves its performance.
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
We hope this work can provide valuable insights to the research of LLM data augmentation.
AgentReview: Exploring Peer Review Dynamics with LLM Agents
Peer review is fundamental to the integrity and advancement of scientific publication.
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
(2) Does fine-tuning LLMs on abstract logic problem generalize to contextualized logic problems and vice versa?
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
Dynamic Evaluation of Large Language Models by Meta Probing Agents
Our multifaceted analysis demonstrated the strong correlation between the basic abilities and an implicit Matthew effect on model size, i. e., larger models possess stronger correlations of the abilities.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
PromptBench: A Unified Library for Evaluation of Large Language Models
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks.
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents
We hope that the framework and environment can be a promising testbed to study competition that fosters understanding of society.
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks.
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
The core idea of RiFT is to exploit the redundant capacity for robustness by fine-tuning the adversarially trained model on its non-robust-critical module.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Furthermore, we present a comprehensive analysis to understand the mystery behind prompt robustness and its transferability.
Cross-Lingual Paraphrase Identification
Machine Translation
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