Title: Silencer: From Discovery to Mitigation of Self-Bias in LLM-as-Benchmark-Generator
Venue: NeurIPS 2025
Accepted: yes
Keywords: large language model

benchmark generation

self-bias
Abstract: LLM-as-Benchmark-Generator methods have been widely studied as a supplement to human annotators for scalable evaluation, while the potential biases within this paradigm remain underexplored. 
In this work, we systematically define and validate the phenomenon of inflated performance in models evaluated on their self-generated benchmarks, referred to as self-bias, and attribute it to sub-biases arising from question domain, language style, and wrong labels.
On this basis, we propose Silencer, a general framework that leverages the heterogeneity between multiple generators at both the sample and benchmark levels to neutralize bias and generate high-quality, self-bias-silenced benchmark. Experimental results across various settings demonstrate that Silencer can suppress self-bias to near zero, significantly improve evaluation effectiveness of the generated benchmark (with an average improvement from 0.655 to 0.833 in Pearson correlation with high-quality human-annotated benchmark), while also exhibiting strong generalizability.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on investigating a timely problem with LLM-generated benchmarks, specifically the self-bias of LLM-as-Benchmark-Generator. In this phenomenon, LLMs tend to achieve overestimated performance on benchmarks they generate themselves. To tackle this problem, the paper first formally defines and quantifies self-bias and empirically demonstrates its prevalence across multiple LLMs and evaluation tasks. Sequentially, the paper heuristically decouples self-bias into three contributors with different relative contributions: question domain bias, language style bias, and wrong label bias. Finally, the paper proposes a new framework, SILENCER, for benchmark generation to address these issues, which mitigates the self-bias at both the sample and benchmark levels. Experimental results on three tasks have validated the effectiveness and generalizability of SILENCER.\n\nSTRENGTHS AND WEAKNESSES:\n**Pros:**\n1. The problem addresses a highly practical and timely issue of NLG evaluation: despite the recent emergence of using LLMs as benchmark generators, potential biases in this paradigm remain underexplored. This paper provides valuable insights into possible biases of LLM-as-Benchmark-Generator and corresponding mitigation strategies.\n2. Overall, the paper is well-written, with clear and logical progression from problem identification and analysis to solution development. Each stage is supported by relevant experiments, effectively substantiating its claims.\n3. The paper conducts extensive experiments, including different models, tasks, and ablation studies. The experimental results sufficiently demonstrate the effectiveness of the proposed SILENCER framework.\n\n**Cons:**\n1. As shown in Figure 1, the self-bias of LLM-as-Benchmark-Generator varies substantially across different base models and tasks. A detailed analysis of these differences would greatly enhance the paper. For instance, the two reasoning models, DeepSeek-Distill-Qwen-32B and QwQ-32B, appear to consistently exhibit the lowest levels of self-bias.  In addition to self-bias, there appears to be evaluation bias between different models as well  (for example, between M1 and M7 in the Math Reasoning task). It would be valuable for the authors to further investigate whether the SILENCER framework is effective in mitigating cross-model evaluation bias.\n2. The decomposition of self-bias in Section 3.2 seems a little ad hoc. The authors could further clarify the completeness and rationality of the sub-bias decomposition empirically or theoretically.\n3. For Section 4.3, considering that the Bias-Neutralizing Ensemble Algorithm is an iterative algorithm, it would be beneficial for the authors to analyze the number of convergence steps and the evolution of self-bias during the iterative process in their experiments.\n\nQUESTIONS:\n1. Missing reference: \n   - AutoBencher: Towards Declarative Benchmark Construction. ICLR 2025.\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper discusses the phenomenon of self-bias in large language models (LLMs) used as benchmark generators, proposing a framework called SILENCER to mitigate this bias and improve evaluation accuracy. The framework includes strategies to mitigate sub-biases at both the sample and benchmark levels. ​ SILENCER employs three main strategies at the sample level:\n\n- Attribute Integration to create domain-unbiased question sets. ​\n- Cross Paraphrase to enhance linguistic diversity and reduce language style bias. ​\n- Label Calibration to improve label accuracy and reduce wrong label bias. ​\n\nAt the benchmark level, a Bias-Neutralizing Ensemble algorithm is used to weigh benchmarks based on their self-bias, improving overall evaluation effectiveness. \n\nSILENCER reduces self-bias to near zero, achieving an average improvement in Pearson correlation from 0.655 to 0.833 with high-quality human-annotated benchmarks. ​The framework is tested across three tasks and seven LLM generators, confirming its generalizability and  effectiveness. Detailed ablation studies validate the contribution of each component in SILENCER, providing insights into the impact of factors like the number of generators and benchmark size on evaluation effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\n- The paper introduces a novel framework, SILENCER, to mitigate self-bias in LLM-as-Benchmark-Generator methods, addressing an under-explored issue in the field. ​\n- The paper provides detailed explanations of the methodology, including formal definitions, experimental settings, and theoretical proofs, ensuring transparency and understanding. ​\n- The work has broad implications for improving the reliability of LLM-generated benchmarks, which are increasingly used in AI research and applications.\n\nQUESTIONS:\nIs there any evaluation on the paraphrases generated wrt quality and correctness? Overall, qualitative assesments are lacking in the paper and could be interesting to look at.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper studies the self-bias of LLM generated benchmarks with respect to the same or similar LLMs to be evaluated or trained on the generated data. Typically, an LLM will be biased to overperform on a benchmark generated by itself. The paper classifies this self-bias into three categories: question domain, language style and labeling and proposes techniques using reference models and human annotated datasets to measure these self-biases. The authors show experimentally the significant self-bias of each LLM in a collection of seven popular LLMs from the industry, on Math Reasoning and Language Understanding datasets. Based on these measures, the paper proposes attribute integration, cross paraphrase and label calibration methods respectively to alleviate the three kinds of self-bias, and further proposes a weighted ensemble approach to combine the benchmarks generated by different LLMs in the collection so that the resultant dataset has low self bias. Experiments on 7 industry LLMs (with 9 others to complete the reference set) are conducted using ablations over baseline generator methods, which show that the three types of self-bias are improved using the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is a fairly comprehensive study of self-bias in LLM generated Benchmarks.\n2. The notions and metrics of self-bias introduced by the paper are natural, well reasoned, and also shown to be prevalent in popular models.\n3. The proposed self-bias mitigation method combines individual approaches targeting each of the categories of self-bias – along with weighted ensembling – and is shown experimentally to significantly decrease the self bias for several LLMs across two tasks.\n\nWeaknesses:\n1. On line 115, the authors state that they use o3-mini as a proxy for human annotators. Could this not make similar models (e.g. those trained on similar datasets) erroneously perform better in terms of self-bias in the experiments.\n2. The presented experimental results seem to have a few gaps: (i) only aggregated bias scores over all generator models are included in Tables 1 and 2, instead of those for each of the seven models and, (ii) the intermediate ablations are not included for AttrPrompt baseline method in Table 2, effectively, the SILENCER method is fully evaluated only for BenchMaker.\n\nQUESTIONS:\n1. It will be helpful to provide examples of generator outputs: question, answer and label for the benchmarks generated/used in the experiments.\n2. Line 241: define $r_p$ formally.\n3. In Table 2, what is the difference between “$\\mathcal{B}^q, \\mathcal{B}^s, \\mathcal{B}^l$ w. Ensemble” and SILENCER ?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates self-bias in benchmarks generated by large language models (LLMs), where an LLM tends to overestimate its own performance on benchmarks it creates due to alignments in question domains, language styles, and labeling errors. It decouples self-bias into sub-components (question domain bias, wrong label bias, and language style bias) and proposes the SILENCER framework to mitigate it. At the sample level, SILENCER introduces three strategies: Attribute Integration (to diversify question domains), Cross Paragraphs (to vary language styles), and Label Calibration (to correct wrong labels). At the benchmark level, it employs a Bias-Neutralizing Ensemble algorithm that weights benchmarks inversely to their estimated self-bias. The framework is evaluated on three tasks (math reasoning, language understanding, commonsense reasoning), seven LLM generators, and two benchmark generation methods, demonstrating a 27.2% average improvement in evaluation consistency with human-annotated benchmarks (Pearson correlation from 0.655 to 0.833).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper explores sources of self-bias in LLM-as-a-judge, which is important but underexplored.\n2. The silencer framework combines sample-level prompting strategies with a benchmark-level ensemble algorithm that dynamically estimates and neutralizes bias.\n3. The experiment result shows lower bias and better consistency with human-annotated benchmarks.\n\n\nWeaknesses:\n1. My main concern is that the experiments adopt o3-mini as a proxy of human annotators. o3-mini still has huge bias.\n2. The experiments are conducted only on three datasets that are not difficult enough. Results may vary on datasets that are super difficult. Besides, these three datasets may not fully capture broader domains (e.g., code generation or multimodal tasks)\n\nQUESTIONS:\n1. Have you compare the annotations of o3-mini with human annotators? \n2. How would results change if genuine human raters supplied labels for a subset of samples? A small‑scale human audit would bolster the claim that Silencer improves true benchmark quality.\n3. Have you tested Silencer on free‑form generation benchmarks (e.g. writing or coding tasks) where label calibration is non‑trivial?\n4. How does Silencer perform on difficult datasets?\n4. The ensemble assumes bias correlates with consistency. What happens when one generator is much weaker (or stronger) than all others?\n\nLIMITATIONS:\nThe authors acknowledge extra compute cost.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-21"}