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Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation

arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the cost of labeling test samples while developing new benchmarks poses a significant challenge, especially when expert annotators are required. Existing frameworks for active sample selection offer limited support for generative Question Answering tasks, where option dynamics can affect model decision boundaries. In this paper, we present Generative Active Testing (GAT), an uncertainty-aware acquisition framework leveraging LLMs as surrogates for informing the sample selection process. Using a novel Statement Adaptation Module, we modify generative tasks into a pseudo-classification format, enabling the capture of sample-level uncertainties across unlabeled candidates. Our zero-shot acquisition functions reduce estimation e

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Aashish Anantha Ramakrishnan, Ardavan Saeedi, Hamid Reza Hassanzadeh, Fazlolah Mohaghegh, Dongwon Lee · · 1 min read · 39 views
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arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the cost of labeling test samples while developing new benchmarks poses a significant challenge, especially when expert annotators are required. Existing frameworks for active sample selection offer limited support for generative Question Answering tasks, where option dynamics can affect model decision boundaries. In this paper, we present Generative Active Testing (GAT), an uncertainty-aware acquisition framework leveraging LLMs as surrogates for informing the sample selection process. Using a novel Statement Adaptation Module, we modify generative tasks into a pseudo-classification format, enabling the capture of sample-level uncertainties across unlabeled candidates. Our zero-shot acquisition functions reduce estimation error by ~40% compared to traditional sampling baselines, offering a scalable solution for cost-effective model benchmarking.

Executive Summary

This article presents Generative Active Testing (GAT), an uncertainty-aware acquisition framework leveraging Large Language Models (LLMs) as surrogates for informing the sample selection process. The proposed method adapts generative tasks into a pseudo-classification format, enabling the capture of sample-level uncertainties across unlabeled candidates. The results show a significant reduction in estimation error (~40% compared to traditional sampling baselines), making GAT a scalable solution for cost-effective model benchmarking in domains such as healthcare and biomedicine. The authors' novel Statement Adaptation Module and zero-shot acquisition functions demonstrate the potential of GAT in addressing the challenge of labeling test samples while developing new benchmarks. The findings have significant implications for the development of efficient LLM evaluation frameworks and highlight the importance of incorporating uncertainty-awareness in active sampling approaches.

Key Points

  • ▸ Generative Active Testing (GAT) is an uncertainty-aware acquisition framework leveraging LLMs as surrogates for informing the sample selection process.
  • ▸ The proposed method adapts generative tasks into a pseudo-classification format, enabling the capture of sample-level uncertainties across unlabeled candidates.
  • ▸ The results show a significant reduction in estimation error (~40% compared to traditional sampling baselines) with GAT.

Merits

Strength in Addressing Labeling Challenges

GAT offers a scalable solution for cost-effective model benchmarking in domains such as healthcare and biomedicine, where labeling test samples can be a significant challenge.

Innovative Application of LLMs

The authors' novel use of LLMs as surrogates for informing the sample selection process demonstrates the potential of GAT in addressing the challenge of labeling test samples while developing new benchmarks.

Uncertainty-Aware Acquisition

The proposed method captures sample-level uncertainties across unlabeled candidates, enabling the development of uncertainty-aware active sampling approaches.

Demerits

Limited Generalizability

The results of GAT may not generalize to other domains or tasks, and further research is needed to evaluate its performance in diverse settings.

Dependence on LLMs

GAT relies on the quality and performance of the LLMs used as surrogates, which may limit its effectiveness if the LLMs are not well-trained or calibrated.

Expert Commentary

The article presents an innovative approach to addressing the challenge of labeling test samples while developing new benchmarks for Large Language Models (LLMs). The proposed Generative Active Testing (GAT) framework leverages LLMs as surrogates for informing the sample selection process, which is a key contribution to the field. However, the limited generalizability of the results and the dependence on LLMs are significant concerns that need to be addressed in future research. Nevertheless, the findings of this study have significant implications for the development of efficient LLM evaluation frameworks and highlight the importance of incorporating uncertainty-awareness in active sampling approaches.

Recommendations

  • ✓ Further research is needed to evaluate the performance of GAT in diverse settings and to address the limitations related to generalizability and dependence on LLMs.
  • ✓ The proposed method should be integrated into existing LLM development and evaluation frameworks to demonstrate its practical applications and scalability.

Sources

Original: arXiv - cs.AI

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