From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning
Pith reviewed 2026-05-16 16:30 UTC · model grok-4.3
The pith
BiForget synthesizes forget sets for LLM unlearning by prompting the target model itself to match its internal knowledge.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
BiForget exploits the target model to elicit forget data that matches its internal knowledge distribution through seed-guided and adversarial prompting, achieving a superior balance of relevance, diversity, and efficiency across benchmarks, such as improving relevance by about 20 and diversity by 0.05 while halving data size in the Harry Potter domain.
What carries the argument
BiForget framework that uses seed-guided and adversarial prompting on the target model to synthesize dual-granularity forget sets matching its internal distribution.
If this is right
- Facilitates more robust forgetting of specific domains or instances.
- Preserves model utility better by using smaller, more targeted forget sets.
- Enables more rigorous evaluation of unlearning methods due to faithful data synthesis.
- Reduces reliance on external data sources for unlearning benchmarks.
Where Pith is reading between the lines
- Applying self-elicited data could minimize distribution shifts in other model modification tasks.
- Testing BiForget on additional domains like medical or code data would reveal its generalizability.
- Combining it with existing unlearning algorithms might further enhance performance.
Load-bearing premise
Prompted data from the target model faithfully represents its internal knowledge distribution without introducing bias or hallucination.
What would settle it
Measuring unlearning success on instances not used in synthesis; if BiForget forget sets do not outperform external ones in forgetting accuracy and utility preservation, the advantage would be disproven.
read the original abstract
Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully represent the true ``forgetting scope'' learned by the model. We formalize two distinct unlearning granularities, domain-level and instance-level, and propose \BiForget, an automated framework for synthesizing high-quality forget sets. Unlike prior work relying on \emph{external} generators, \BiForget exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. Our experiments across diverse benchmarks show that it achieves a superior balance of relevance, diversity, and efficiency. Quantitatively, in the Harry Potter domain, it improves relevance by ${\sim}20$ and diversity by ${\sim}$0.05 while \emph{halving} the total data size compared to SOTAs. Ultimately, it facilitates more robust forgetting and better utility preservation, providing a more rigorous foundation for evaluating LLM unlearning.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces BiForget, an automated framework for synthesizing forget sets for LLM unlearning at dual granularities (domain-level and instance-level). It uses the target model itself via seed-guided and adversarial prompting to generate data that matches its internal knowledge distribution, claiming superior relevance, diversity, and efficiency over prior methods relying on external generators. Experiments on benchmarks like the Harry Potter domain report improvements of approximately 20 in relevance and 0.05 in diversity while halving data size, leading to better forgetting robustness and utility preservation.
Significance. If the experimental claims hold after proper validation, BiForget could advance LLM unlearning research by addressing gaps in benchmark fidelity through model-elicited data synthesis. The dual-granularity formalization is a clear conceptual contribution, and the efficiency gains (halved data size) would be practically useful if reproducibility is established. The work is grounded in a plausible hypothesis but currently lacks the experimental rigor needed for strong impact.
major comments (2)
- [Abstract and Experiments] Abstract and Experiments section: The reported gains (relevance improved by ~20, diversity by ~0.05, data size halved) are presented without defining the underlying metrics, listing exact baseline methods and their scores, reporting statistical tests or variance, or describing prompting templates and controls for model artifacts; this directly weakens the central claim of superior balance.
- [Methodology] Methodology (BiForget framework description): The assumption that seed-guided and adversarial prompting of the target model produces data faithfully matching its internal knowledge distribution is load-bearing for all downstream claims but receives no validation, such as overlap analysis with known memorized content, human coverage checks, or ablation on hallucination rates.
minor comments (2)
- [Throughout] Ensure consistent formatting of the framework name (BiForget) and any equations for data synthesis across sections.
- [Related Work] Add explicit references to prior unlearning benchmarks and external-generator baselines in the related-work discussion.
Simulated Author's Rebuttal
We thank the referee for their detailed feedback on our manuscript. We appreciate the opportunity to clarify and strengthen our presentation of the BiForget framework and its experimental validation. Below, we address each major comment point by point.
read point-by-point responses
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Referee: [Abstract and Experiments] Abstract and Experiments section: The reported gains (relevance improved by ~20, diversity by ~0.05, data size halved) are presented without defining the underlying metrics, listing exact baseline methods and their scores, reporting statistical tests or variance, or describing prompting templates and controls for model artifacts; this directly weakens the central claim of superior balance.
Authors: We agree that additional details are necessary to fully substantiate the reported gains. In the revised version, we will define the relevance metric (as the average semantic similarity score between generated forget instances and the model's internal knowledge representations, measured via embedding cosine similarity) and the diversity metric (as the normalized entropy of n-gram distributions across the dataset). We will include a comprehensive table in the experiments section listing all baseline methods with their exact scores for relevance, diversity, and data size. Statistical significance will be reported using paired t-tests with p-values and standard deviations across 5 random seeds. Prompting templates and controls (e.g., temperature settings, repetition penalties to mitigate artifacts) will be detailed in a new appendix section. These changes will make the superiority claims more transparent and verifiable. revision: yes
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Referee: [Methodology] Methodology (BiForget framework description): The assumption that seed-guided and adversarial prompting of the target model produces data faithfully matching its internal knowledge distribution is load-bearing for all downstream claims but receives no validation, such as overlap analysis with known memorized content, human coverage checks, or ablation on hallucination rates.
Authors: The design of BiForget is predicated on the target model's ability to generate data reflective of its learned knowledge through targeted prompting. While the current manuscript relies on downstream unlearning performance as indirect evidence, we recognize the value of direct validation. We will incorporate in the revised manuscript: (1) an overlap analysis measuring the intersection between generated forget sets and a set of known memorized facts from the target model (using both exact string matching and semantic similarity thresholds); (2) results from human annotators assessing coverage of domain knowledge; and (3) an ablation study quantifying hallucination rates by evaluating the factual correctness of generated instances against ground-truth sources. These additions will provide direct support for the assumption while preserving the original experimental outcomes. revision: yes
Circularity Check
No circularity in BiForget synthesis or evaluation chain
full rationale
The paper defines BiForget as an automated synthesis framework that elicits forget data from the target LLM itself via seed-guided and adversarial prompting to match its internal distribution, then empirically compares resulting relevance (~20 gain), diversity (~0.05 gain), and halved data size against external SOTA baselines on benchmarks such as Harry Potter. No equations, fitted parameters, or self-citations are shown that would make the claimed improvements reduce to the inputs by construction; the synthesis method and quantitative metrics remain independent of the downstream unlearning results.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Responses from the target model to seed-guided and adversarial prompts accurately reflect its internal knowledge distribution.
invented entities (1)
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BiForget framework
no independent evidence
discussion (0)
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