Title: Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM-contamination

crowdsourcing

peer prediction

data evaluation
Abstract: The recent success of generative AI highlights the crucial role of high-quality human feedback in building trustworthy AI systems. However, the increasing use of large language models (LLMs) by crowdsourcing workers poses a significant challenge: datasets intended to reflect human input may be compromised by LLM-generated responses. Existing LLM detection approaches often rely on high-dimensional training data such as text, making them unsuitable for structured annotation tasks like multiple-choice labeling. In this work, we investigate the potential of peer prediction --- a mechanism that evaluates the information within workers' responses --- to mitigate LLM-assisted cheating in crowdsourcing with a focus on annotation tasks. Our method quantifies the correlations between worker answers while conditioning on (a subset of) LLM-generated labels available to the requester. Building on prior research, we propose a training-free scoring mechanism with theoretical guarantees under a novel model that accounts for LLM collusion. We establish conditions under which our method is effective and empirically demonstrate its robustness in detecting low-effort cheating on real-world crowdsourcing datasets.


Peer reviews:
comments: SUMMARY:
This research aims to address the impact of Large Language Model (LLM)-assisted cheating on the data quality of crowdsourcing. Traditional methods for dealing with low-quality workers in crowdsourcing often rely on correlations among workers' responses. However, such methods are ill-suited for situations where correlations arise from the use of LLM-generated outputs.

To overcome this, this study adopts an approach that utilizes correlations between workers' responses, conditioned on LLM-generated outputs. By doing so, the paper proposes a theoretically grounded quality measure for crowdsourced annotation tasks. The effectiveness of this approach is further evaluated through empirical experiments.

STRENGTHS AND WEAKNESSES:
This paper appears to be very well-written in my assessment. The motivation is clear, and I believe the content is a good fit for this conference.
A weakness of this paper is that its primary focus is on crowdsourcing datasets involving subjective questions or tasks where human experts are considered the gold standard. With the recent advancements in LLMs, the scope of applicable tasks might decrease over time.

Nevertheless, overall, I consider this to be a good paper.

QUESTIONS:
N/A

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-09

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes theoretically grounded quality measure method for annotation tasks, called the conditioned correlated agreement (CA) mechanism. The method quantifies the correlations between worker answers while conditioning on (a subset of) LLM-generated labels available to the requester. Building on prior research, the proposed training-free scoring mechanism is theoretical guaranteed under a novel model that accounts for LLM collusion. Experimental results show that their method is effective in identifying LLM-assisted cheating when conditioning on the “right” model.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. Compared with existing LLM detection methods, the method proposed in this paper is based on peer prediction and is suitable for structured annotation tasks like multiple-choice labeling.\n\n2. The proposed conditioned CA mechanism is information monotone under naive strategies, which motivates workers to truthfully report their high-effort signals.\n\n3. Experiments demonstrates that the proposed conditioned CA mechanism is robust in detecting low-effort cheating on real-world crowdsourcing datasets.\n\nWeaknesses:\n1. Figure 2 only shows the results on (a) Hatefulness and (b) Preference Alignment. Why not directly show the results on (a) Hatefulness, (b) Offensiveness, (c) Toxicity and (d) Preference Alignment? As the extension of Figure 2, Figure 4 shows the results on Hatefulness again, but the result is different from Figure 2. Yet at the same time, the result on Toxicity is not shown. Please check if the figures are incorrectly labeled.\n\n2. In Figure 2's legend, “Expert”, “Normal worker”, and “Human” refer to X_i according to the “Assumption Verification” subsection, which is inconsistent with the description of Figure 2’s caption.\n\n3. In Figure 3b, it is not clear which methods the different colored lines correspond to respectively. Please provide a legend.\n\n4. In subsection 5.3, the metric chosen to measure the effectiveness of proposed method to identify cheating agents with naive strategies is AUC. How does AUC reflect the effectiveness of detecting cheating agents?\n\nQUESTIONS:\nPlease refer to the weaknesses above.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a peer prediction-based mechanism to detect LLM-assisted cheating in crowdsourced annotation tasks where ground truth is unavailable. The core hypothesis posits that authentic human-generated responses exhibit distinct correlational patterns irreducible to LLM outputs. The introduced Conditioned Correlated Agreement (CAz) mechanism evaluates worker responses by quantifying inter-annotator correlations conditioned on requester-accessible LLM-generated signals (Z)\nThe paper addresses a timely problem with a theoretically grounded and empirically validated method. While assumptions require relaxation and scope expansion needs further validation, the CAz mechanism represents a significant advance in crowdsourcing data integrity preservation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. Addresses the critical challenge of data integrity erosion due to undetected LLM usage in human feedback pipelines, particularly for tasks reliant on subjective human judgment (e.g., preference labeling, toxicity assessment).\n2. The CAz mechanism provides a training-free, ground-truth-agnostic framework leveraging requester-generated LLM signals to disentangle genuine human effort from LLM-derived responses.\n\n**Weaknesses**\n\n1. The core requirement of this paper is that the principal can generate labels with an LLM $z$ to use for conditioning. This hinges on a crucial assumption: the principal knows which LLMs the cheaters are likely to use. If cheaters use a different proprietary model that the principal doesn't have access to, the effectiveness of the conditioning step is questionable. And\n2. The strongest guarantees (for e-information monotonicity against any cheating strategy) rely on Assumption 4.1 and 4.2. However, the papers own empirical tests in Section 5.3 show that these assumptions \"generally do not hold in practice\". The authors then fall back on the weaker Assumption 4.5, which only provides guarantees against \"naive\" cheating strategies.\n3. When the fraction of LLM cheaters is very low, the performance of the method degraded to traditional methods. The scene of this CAz can be clarified more specifically.\n4. The further requirements of LLM data annotation would turn to more complex and open-ended text, which can exceed the tasks analyzed in this paper.\n\nQUESTIONS:\nHow might CAz be adapted for high-dimensional, open-ended responses (e.g., peer reviews, creative writing) while maintaining theoretical guarantees? Does the embedding projection heuristic preserve information monotonicity properties?\n\nLIMITATIONS:\n1. The major concern is still the blurring line between \"Tool Use\" and \"Cheating\": we are all becoming cyborgs in our daily work. At what point does using an LLM to assist with a task cross the line from acceptable tool use to data-corrupting cheating? Or, shouldn't we towards a more clear and inspired benchmarking that show the more interesting distribution that we want AI to be but not just reject something?\n2. Is LLM-Polished Data bad? The paper doesn't provide a framework for navigating this gray area of legitimate augmentation versus outright replacement.\n3. The risk of penalizing Neurodiversity and creative outliers. The method inherently rewards workers whose judgments align with the \"diligent majority.\" What about a worker who is a genuine human but has a unique, outlier perspective? Or someone who interprets the task instructions in an unconventional but still valid way? Their responses might not correlate well with others, leading the CAz method to incorrectly flag them as low-effort or a \"cheater.\" The system could inadvertently filter out cognitive diversity, which is often the very thing subjective tasks are trying to capture.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper develops a theoretically robust quality measure to evaluate the quality of human contributions in addition to existing LLM answers and detect workers with less informative responses.

STRENGTHS AND WEAKNESSES:
Strengths:
- The paper studies an important task. The method is well-motivated and well-thought-out.
- The paper provided interesting insights regarding the pattern of human labelers and LLM labelers. 
- The paper is well-written and clear.

The method is developed with a realistic setting and moderate assumptions, a good improvement over previous methods. The authors made it very clear about the scope and how to interpret the results, which I appreciate a lot. The writing is nice and easy to follow, with sufficient explanations and motivations. The experiments are well-designed and validate the claims made earlier.

Overall, I think it is a good paper.

QUESTIONS:
What is the computational cost of the score?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-02