Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation (2412.15255v2)

Abstract: In this paper, we show that knowledge distillation can be subverted to manipulate LLM benchmark scores, revealing a critical vulnerability in current evaluation practices. We introduce "Data Laundering," a process that enables the covert transfer of benchmark-specific knowledge through seemingly legitimate intermediate training steps. Through extensive experiments with a 2-layer BERT student model, we show how this approach can achieve substantial improvements in benchmark accuracy (up to 75\% on GPQA) without developing genuine reasoning capabilities. Notably, this method can be exploited intentionally or even unintentionally, as researchers may inadvertently adopt this method and inflate scores without realising the implications. While our findings demonstrate the effectiveness of this technique, we present them as a cautionary tale highlighting the urgent need for more robust evaluation methods in AI. This work aims to contribute to the ongoing discussion about evaluation integrity in AI development and the need for benchmarks that more accurately reflect true model capabilities. The code is available at https://github.com/mbzuai-nlp/data_laundering.

• The paper introduces "Data Laundering," a vulnerability where knowledge distillation artificially boosts language model benchmark performance by subtly transferring test-specific knowledge.
• Data Laundering involves three phases—placement, layering, and integration—to transfer benchmark knowledge to a student model through intermediate training.
• Experiments show significant benchmark gains (e.g., up to 75% on GPQA) without actual skill improvement, highlighting the urgent need for more robust evaluation frameworks.

Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation

The paper "Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation" by Mansurov et al. presents a detailed examination of a potential vulnerability in current LLM evaluation practices. The authors introduce the notion of "Data Laundering," an intentional or inadvertent exploitation of knowledge distillation to enhance LLM performance on benchmarks artificially. This research underscores the urgent need for more robust evaluation frameworks to maintain the integrity of AI development and benchmark accuracy.

Core Contribution

This paper advances the understanding of benchmark vulnerabilities by demonstrating the concept of "Data Laundering." In this context, knowledge distillation, a method initially designed for model compression and transfer learning, is repurposed to unobtrusively transmit benchmark-specific knowledge through intermediate training phases. The process is structured into three phases—placement, layering, and integration—mirroring the systematic disguise of data origins akin to financial money laundering.

1. Placement Phase: Knowledge specific to benchmarks is inserted into a teacher model that has been illicitly trained on test data. This stage establishes an "unfair" knowledge base, which is covertly transmitted to the student model.
2. Layering Phase: Using intermediate datasets and knowledge distillation, knowledge is transferred to a student model without direct access to the test data. The combination of hard and soft label learning restricts transparency of knowledge origin.
3. Integration Phase: The student model is ultimately evaluated on the original benchmarks to validate the performance improvement, which ostensibly appears as genuine skill acquisition.

Experimental Results

Through meticulous experimentation, including the use of a 2-layer BERT student model, the authors demonstrate that significant enhancements in benchmark accuracy (e.g., up to 75% on the GPQA task) can be achieved without actual advancement in intrinsic reasoning abilities.

• Upon applying Data Laundering, the BERT model neared state-of-the-art performance on the GPQA benchmark while maintaining fairness boundaries through knowledge distillation.
• The involvement of different architectures and dataset sizes reinforced the hypothesized impact of intermediate data selection on final performance. The MedMCQA and RACE datasets exemplified different domain alignments during the distillation process, influencing performance across benchmarks.

Notably, the findings revealed that despite smaller dataset sizes or extreme modifications of intermediate datasets, benchmark knowledge can still leak through structural patterns intrinsic to training data formats.

Implications and Future Research

This paper highlights the critical issue of benchmark score inflation, emphasizing the detachment between apparent LLM progress and authentic capability improvement. It also raises concerns about evaluation integrity, especially when researchers unknowingly use teacher models trained on contaminated data, potentially leading to manipulated outcomes.

The implications of these outcomes necessitate more rigorous benchmark design and evaluation methods to better guard against subtle forms of benchmark gaming. Future research should explore the development of more resilient benchmarks, potentially integrating private evaluation structures to curb data contamination. Additionally, an exploration into alternative definitions of knowledge memorization, as the verbatim memorization examined in this paper, proves insufficient, presenting potential future avenues to explore more nuanced methodologies.

By exposing these vulnerabilities, the paper serves as a pivotal reference point for ongoing discourse on evaluation integrity in AI, encouraging researchers to adopt measures that reflect true advances rather than superficial gain facilitated through methodological exploitation.