FedEval-LLM: A Federated Evaluation Framework for Assessing the Performance of Large Language Models on Downstream Tasks Using Collective Wisdom

Kernekoncepter

FedEval-LLM provides a reliable and privacy-preserving framework for evaluating the performance of large language models on downstream tasks by leveraging the collective wisdom of participating clients through personalized evaluation models.

Resumé

The article proposes FedEval-LLM, a federated evaluation framework for assessing the performance of large language models (LLMs) on downstream tasks. The key highlights are:

Limitations of traditional evaluation methods: Existing methods rely on labeled test sets or external advanced LLMs, which fail to accurately reflect the performance of LLMs on generative tasks and raise privacy concerns.

Personalized evaluation models: FedEval-LLM trains personalized evaluation models for each client by leveraging their local data and a bootstrapped task-specific evaluation dataset. This allows the evaluation models to align with the specific requirements of the downstream tasks.

Collective evaluation: FedEval-LLM employs a group of personalized evaluation models as referees to provide a reliable and unbiased assessment of the global model's performance. This mitigates the limitations of a single evaluation model.

Experimental results: The proposed framework demonstrates significant improvements in evaluation capability compared to traditional methods. When applied to a federated learning scenario, FedEval-LLM exhibits strong agreement with human preference and RougeL-score on carefully curated test sets, while providing strong privacy-preserving capabilities.

Importance of domain knowledge: The experiments highlight the critical role of domain knowledge in achieving accurate evaluation on downstream tasks. Training on data from the target domain and using in-domain evaluation data are essential for gaining task-specific evaluation capabilities.

Privacy preservation: FedEval-LLM addresses privacy concerns by eliminating the need for external services and labeled test sets, thereby mitigating the risk of data leakage.

Statistik

"The performance of various LLMs measured by RougeL-score reveals a significant dependence on the test datasets and does not align well with human preference."
"Utilizing a group of LLMs as referees is essential for both selecting high-evaluation data and providing reliable evaluation."

Citater

"Traditional methods typically leverage labeled test sets to evaluate generative tasks. However, these test sets, typically composed of one-to-one question-answer (QA) pairs, capture only a fraction of acceptable answers, thereby incapable of providing a reliable evaluation of LLM's performance."
"To tackle this problem, we propose FedEval-LLM, a Federated Evaluation framework for LLM (depicted in Fig. 1), designed to provide reliable performance assessment of LLMs on downstream tasks."
"Consequently, by leveraging the collective wisdom of participating clients, we transform local knowledge into task-specific evaluation models. Utilizing these evaluation models as a collective group provides reliable evaluation capability on the target task under the FL framework."

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FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom

by Yuanqin He,Y... kl. arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12273.pdf

Dybere Forespørgsler

How can the FedEval-LLM framework be extended to handle more complex downstream tasks, such as open-ended dialogue or multi-turn interactions?

The FedEval-LLM framework can be extended to handle more complex downstream tasks by incorporating advanced techniques and strategies tailored to the specific requirements of tasks like open-ended dialogue or multi-turn interactions. Here are some key ways to enhance the framework for such tasks:

Contextual Understanding: For tasks involving open-ended dialogue or multi-turn interactions, the evaluation models need to have a deep understanding of context and continuity in conversations. This can be achieved by training the personalized evaluation models on datasets that emphasize dialogue flow and context preservation.

Sequential Evaluation: Implementing a sequential evaluation mechanism where the evaluation models consider the entire conversation history rather than individual responses can enhance the evaluation accuracy for multi-turn interactions. This sequential evaluation can capture the coherence and relevance of responses over multiple turns.

Dynamic Prompting: Introducing dynamic prompting techniques where the evaluation prompts adapt based on the ongoing conversation can improve the evaluation models' ability to assess the quality of responses in real-time dialogue scenarios. This dynamic prompting can guide the evaluation models to focus on relevant aspects of the conversation.

Multi-Modal Evaluation: Incorporating multi-modal inputs, such as text, images, or audio, can enrich the evaluation process for tasks involving diverse data types in open-ended dialogue or multi-turn interactions. The evaluation models can be trained to evaluate responses based on a combination of modalities.

Adversarial Evaluation: Implementing adversarial evaluation techniques where the evaluation models are challenged with generating responses that deceive other models can enhance the robustness of the framework for complex tasks. This adversarial training can help identify weaknesses and improve the overall evaluation capability.

By integrating these advanced strategies and techniques tailored to the nuances of open-ended dialogue and multi-turn interactions, the FedEval-LLM framework can effectively handle more complex downstream tasks with improved accuracy and reliability.

What are the potential limitations or drawbacks of the collective evaluation approach, and how can they be addressed to further improve the reliability and robustness of the framework?

While the collective evaluation approach in the FedEval-LLM framework offers several benefits, there are potential limitations and drawbacks that need to be addressed to enhance the reliability and robustness of the framework:

Bias and Groupthink: One limitation of collective evaluation is the potential for bias or groupthink among the referees, leading to skewed evaluation results. To address this, introducing diversity among the referees by incorporating a broader range of perspectives and training data can help mitigate bias and enhance the reliability of evaluations.

Scalability: As the number of participants and evaluation models increases, scalability can become a challenge in the collective evaluation approach. Implementing efficient communication protocols and distributed computing strategies can address scalability issues and ensure smooth coordination among the referees.

Consensus Building: Achieving consensus among multiple referees with diverse opinions can be challenging. Implementing robust aggregation mechanisms, such as advanced voting algorithms or weighted averaging based on referee expertise, can facilitate consensus building and improve the reliability of evaluation outcomes.

Evaluation Quality Control: Ensuring the quality and consistency of evaluations across multiple referees is crucial for reliable results. Implementing stringent quality control measures, such as regular calibration sessions, feedback loops, and performance monitoring, can help maintain evaluation standards and enhance the robustness of the framework.

Privacy Concerns: In a collective evaluation setting, privacy concerns related to sharing evaluation data among participants need to be addressed. Implementing secure data sharing protocols, encryption techniques, and anonymization methods can safeguard sensitive information and uphold data privacy in the evaluation process.

By proactively addressing these limitations and drawbacks through targeted strategies and mechanisms, the collective evaluation approach in the FedEval-LLM framework can be strengthened to improve reliability, accuracy, and robustness in evaluating large language models on downstream tasks.

Given the importance of domain knowledge for accurate evaluation, how can the FedEval-LLM framework be adapted to effectively leverage and integrate diverse domain knowledge from multiple participants in a federated setting?

To effectively leverage and integrate diverse domain knowledge from multiple participants in a federated setting within the FedEval-LLM framework, the following strategies can be implemented:

Domain-Specific Evaluation Models: Develop personalized evaluation models for each participant based on their domain expertise and local data. By training evaluation models on task-specific datasets, participants can contribute their domain knowledge to the collective evaluation process, ensuring alignment with the evaluation criteria of downstream tasks.

Domain-Specific Prompting: Tailor the evaluation prompts and criteria to reflect the specific requirements of different domains. Participants can provide domain-specific prompts and guidelines for evaluation, enabling the evaluation models to focus on relevant aspects of the tasks within their respective domains.

Consensus Building on Domain Knowledge: Encourage collaboration and consensus building among participants to harmonize diverse domain knowledge and perspectives. By incorporating feedback loops, calibration sessions, and knowledge sharing sessions, participants can collectively refine the evaluation criteria and enhance the quality of domain-specific evaluations.

Multi-Domain Evaluation Panels: Form multi-domain evaluation panels comprising participants with expertise in different domains. By aggregating evaluations from diverse panels, the FedEval-LLM framework can capture a comprehensive range of domain knowledge and perspectives, leading to more robust and reliable evaluation outcomes.

Continuous Learning and Adaptation: Implement mechanisms for continuous learning and adaptation within the framework to incorporate evolving domain knowledge and insights from participants. By facilitating ongoing training and updates to the evaluation models based on new domain data and feedback, the framework can stay current and effective in leveraging diverse domain knowledge.

Privacy-Preserving Domain Collaboration: Ensure privacy-preserving protocols and mechanisms are in place to facilitate secure sharing of domain knowledge among participants. By safeguarding sensitive domain data and upholding data privacy, participants can confidently contribute their expertise to the collective evaluation process without compromising confidentiality.

By implementing these strategies and fostering a collaborative environment that values diverse domain knowledge, the FedEval-LLM framework can effectively leverage and integrate varied domain expertise from multiple participants in a federated setting, enhancing the evaluation capability and accuracy of large language models on downstream tasks.