Data Swarms: Optimizable Generation of Synthetic Evaluation Data (2506.00741v2)

Abstract: We propose Data Swarms, an algorithm to optimize the generation of synthetic evaluation data and advance quantitative desiderata of LLM evaluation. We first train a swarm of initial data generators using existing data, and define various evaluation objectives to reflect the desired properties of evaluation (e.g., generate more difficult problems for the evaluated models) and quantitatively evaluate data generators. We then employ particle swarm optimization to optimize the swarm of data generators, where they collaboratively search through the model parameter space to find new generators that advance these objectives. We further extend it to Adversarial Swarms, where the data generator swarm generates harder data while the test taker model swarm learns from such data, co-evolving dynamically for better data and models simultaneously. Extensive experiments demonstrate that Data Swarms outperforms eight data generation baselines across five evaluation objectives, while Adversarial Swarms produce more robust learning of synthetic data and stronger generalization. Further analysis reveals that Data Swarms successfully optimizes compositions of multiple evaluation objectives and generalizes to new off-the-shelf LLMs, unseen at optimization time.

An Expert Overview of "Data Swarms: Optimizable Generation of Synthetic Evaluation Data"

The paper "Data Swarms: Optimizable Generation of Synthetic Evaluation Data" introduces an innovative approach for generating synthetic evaluation data designed to improve the assessment of LLMs. Recognizing the limitations of static evaluation data, the authors propose a dynamic system termed Data Swarms that employs swarm intelligence to optimize the generation of synthetic evaluation data according to specified quantitative objectives.

Core Concept

The primary contribution of the paper lies in the introduction of the Data Swarms algorithm, which utilizes Particle Swarm Optimization (PSO) to optimize a swarm of data generator models. Starting from an initial swarm of data generators trained using existing datasets, these generators undergo iterative optimization to meet multi-faceted evaluation objectives. Importantly, five key objectives are defined to guide this optimization: difficulty, separation, novelty, consistency, and personalization. The methodology is further extended to form Adversarial Swarms, where the data generator swarm and test taker model swarm co-evolve to produce increasingly challenging synthetic data and enhance model capabilities concurrently.

Methodological Insights

The paper delineates a thorough methodological framework involving several stages:

1. Initialization: Data generators are initially trained using self-instruct techniques on clustered subsets of seed data, capturing diverse evaluation aspects.
2. Objective Definition and Evaluation: The authors define distinct objectives such as generating difficult data to expose model weaknesses and separate data to widen performance gaps among models. The novelty of generated data is quantified in terms of deviation from existing data insights.
3. Optimization Process: A PSO-based approach is employed where each data generator interacts with both individual and swarm-level intelligence signals to explore model weight space, iteratively optimizing towards the defined objectives.
4. Adversarial Co-Evolution: Adversarial Swarms facilitate a competitive dynamic where models continuously adapt to harder synthetic data, leading to the progressive enhancement of both data and model efficacy.

Empirical Validation

Extensive experiments demonstrate the superiority of Data Swarms over eight data generation baselines across multiple evaluation objectives and domains. Strong numerical results indicate substantial improvements in generating difficult and novel problems, particularly in mathematical reasoning tasks where Data Swarms produce longer and more compositional queries.

Theoretical and Practical Implications

The research carries significant theoretical implications by presenting a novel optimization perspective on synthetic data generation—a move away from heuristic-driven methods towards quantifiable, objective-based strategies. Practically, Data Swarms facilitate scalable synthetic data generation, effectively tailoring evaluation problems to evolving model capabilities and mitigating static dataset saturation concerns.

Future Directions

The paper opens avenues for further exploration into adaptable synthetic data generation frameworks and the refinement of evaluation objectives tailored to specific LLM capabilities. Investigation into alternative optimization algorithms and the integration of additional evaluation domains could further sophisticate the Data Swarms paradigm. Moreover, addressing computational efficiency and scalability for larger model sizes remains a critical focus for future research.

Conclusion

"Data Swarms: Optimizable Generation of Synthetic Evaluation Data" marks a significant contribution to LLM evaluation methodologies, offering a robust, objective-driven synthetic data generation framework that aligns closely with real-world application needs. It sets a foundation for continuous adaptation and optimization, challenging the status quo of static evaluation to support the dynamic landscape of AI development.