Title: Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Shortcut Learning

Injection Attack

Misinformation Detection
Abstract: Misinformation detectors often rely on superficial cues (i.e., shortcuts) that correlate with misinformation in training data but fail to generalize to the diverse and evolving nature of real-world misinformation. This issue is exacerbated by large language models (LLMs), which can easily generate convincing misinformation using simple prompts. We introduce TruthOverTricks, a unified evaluation paradigm for measuring shortcut learning in misinformation detection. TruthOverTricks categorizes shortcut behaviors into intrinsic shortcut induction and extrinsic shortcut injection, and evaluates seven representative detectors across 14 popular benchmarks, along with two new factual misinformation datasets, NQ-Misinfo and Streaming-Misinfo. Empirical results reveal that existing detectors suffer severe performance degradation when exposed to both naturally occurring and adversarially crafted shortcuts. To address this, we propose the Shortcut Mitigation Framework (SMF), an LLM-augmented data augmentation framework that mitigates shortcut reliance through paraphrasing, factual summarization, and sentiment normalization. SMF consistently enhances robustness across 16 benchmarks, forcing models to rely on deeper semantic understanding rather than shortcut cues.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates how biases (i.e, shortcuts) in misinformation classification datasets deteriorate the performance. It proceeds to propose a method using LLM data augmentation to counteract these biases. Overall, it supports its claims by studying various benchmarks and models.\n\nSTRENGTHS AND WEAKNESSES:\nI like the paper and the paper setup. The paper presents a thorough overall evaluation. It employs human evaluation for the critical parts of the paper, namely injection fidelity and authenticity, when employing LLMs to alter texts. It also employs a vast number of models and benchmarks to support its main claims. This leads me to believe that the claims made in the paper are actually valid.\n\nHowever, I have to outline one major weakness. While the buildup of the hypothesis is very good (i.e., chapters 1-3), it is unfortunate to leave out LLMs in the Shortcut Mitigation Framework (SMF) (chapter 4). Your argumentation states that LLM-based detectors are already robust to shortcuts. This opens up two problems. First, while your method might solve a lot of issues for the other models, it might also introduce some areas where, actually, these \"shortcuts\"/cues are the only indicators. Thus, your method might decrease the scores for LLMs or cause other problems we don't know of yet. This should be investigated. It is not a big problem because then SMF is only suitable for simpler methods but it would reveal interesting dynamics when using SMF. I assume that discovering the up- and downsides of SMF more thoroughly would help in inspiring the community or yourselves to come up with an even better approach. Second, leaving out LLMs basically means that we do not know whether your method improves the SOTA. While it is not critical that your method improves the SOTA against LLMs, it is crucial for users to understand this. I would recommend exploring this. SMF is a major contribution here, and I think it is not yet fully explored.\n\nA minor point that would be interesting is how you deal with the fact that the SMF might reject rephrasing misinformation (as indicated in lines 138-140). What does this mean for SMF as a pipeline? Do you disregard these claims?\n\nMy conclusion: While I believe in the points made, I think important to understand the driving factors of the SMF and LLM investigations may reveal that. Does SMF solve some problems, but cause others? I believe all claims made, but those that are not made are very important as well.\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nsee above", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a framework designed to evaluate whether misinformation detection models rely on spurious correlations (such as sentiment, style, or topic) instead of factual content. The framework tests models in two settings: intrinsic shortcut induction, where natural biases in existing datasets are isolated, and extrinsic shortcut injection, where LLMs are used to rewrite content with specific superficial features to simulate adversarial attacks. The authors show that many models degrade significantly under these conditions, revealing a strong reliance on shortcuts. To address this, they propose SMF, an LLM-based data augmentation method that removes shortcut cues through paraphrasing, summarization, or style normalization. Experiments across 16 datasets demonstrate that SMF improves model robustness and encourages more reliable detection of misinformation across varied contexts.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n\n- The research is very well motivated, very well written and easy to follow. Probably also easy to replicate given the simplistic nature. \n- The novel contributions of the paper include the TRUTHOVERTRICKS framework, two factual misinformation benchmarks and a lightweight mitigation approach that improves model robustness by rewriting training data to reduce reliance on superficial features.\n- A systematic evaluation of shortcuts is conducted by looking into the surface level features in existing datasets as well as by injecting shortcuts using LLMs. The method is quite intuitive and can easily be adapted to other tasks like deception detection.\n- Extensive evaluation is conducted across datasets and models.\n\n\n### Weaknesses:\n- The paper discusses 4 shortcut types which are well evaluated and intuitive. However there is no discussion of other potential shortcuts like reliance on entities or persuasion strategies.\n- It is unclear from the main text how the failure in implicit injection was handled and how this influences the overall study.\n\nQUESTIONS:\n- Referring to lines 136-140, how did you handle the cases with (1)low agreement scores in implicit injection and (2) LLM refusal ?\n\n\n\n### Minor  things:\n- The expansion of SMF is not mentioned in the first use of the term in abstract or introduction\n\nLIMITATIONS:\nYes. Included in the appendix", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces TRUTHOVERTRICKS, a comprehensive evaluation framework for measuring and mitigating shortcut learning in misinformation detection systems. The authors identify that current misinformation detectors often rely on superficial cues (shortcuts) like sentiment, style, topic, and perplexity rather than genuine content authenticity. They evaluate seven detectors across 16 datasets, create two new factual misinformation datasets, and propose SMF (Shortcut Mitigation Framework) - an LLM-based data augmentation approach to improve detector robustness.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n* Novel Factual Datasets: The creation of NQ-Misinfo and Streaming-Misinfo datasets that require factual knowledge for detection fills an important gap in existing benchmarks.\n\n* Practical Solution: The SMF framework offers a model-agnostic approach to improving detector robustness through data augmentation rather than complex model modifications.\n\n* Strong Empirical Results: The paper demonstrates significant performance drops when detectors encounter distributional shifts, and shows SMF can improve robustness substantially.\n\n**Weaknesses:**\n\n* Problematic \"Shortcut\" Framing: The core conceptual issue is treating established, validated features (sentiment, style, topic, perplexity) as \"shortcuts\" when prior work [1] [2] have demonstrated their legitimate utility for misinformation detection. The paper lacks adequate justification for why these should be considered spurious rather than valid but insufficient features.\n\n* Artificial Evaluation Setup: The experimental design creates extreme distributional mismatches that may not reflect realistic deployment scenarios. The performance drops might indicate normal sensitivity to distribution shift rather than problematic shortcut learning.\n\n* Missing Causal Analysis: The paper provides insufficient evidence that the studied features lack causal relationships with misinformation, which is necessary to classify them as \"shortcuts.\"\n\n[1] Cui, Limeng, Suhang Wang, and Dongwon Lee. \"Same: sentiment-aware multi-modal embedding for detecting fake news.\" In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp. 41-48. 2019.\n\n[2] Przybyla, Piotr. \"Capturing the style of fake news.\" In Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 01, pp. 490-497. 2020.\n\nQUESTIONS:\nHow do you justify classifying sentiment, style, topic, and perplexity as \"shortcuts\" when extensive prior work has demonstrated their legitimate utility for misinformation detection?\n\nTo what extent do your experimental setups reflect realistic deployment scenarios where detectors would encounter misinformation?\n\nUnder what conditions should practitioners apply your SMF framework, and when might it actually hurt performance? Since you're essentially removing potentially useful features, how do practitioners determine whether their specific use case suffers from harmful shortcut learning versus beneficial feature utilization?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAuthors investigate the significance of shortcut learning in detecting misinformation in LLMs, mid-sized LMs, and ad hoc methods. The define two groups of potential shortcuts: intrinsic and extrinsic. Intrinsic includes sentiment, style, topic, and perplexity; extrinsic includes rewriting, explicit, and implicit. \n\nFor the intrinsic shortcuts, given 16 datasets (including 2 repurposed datasets) they selectively form training and test sets and show that the mentioned detection method significantly rely on the discussed shortcut to make their prediction. For the extrinsic shortcuts, they use an LLM and create synthetic sets for the mentioned datasets, and again reach the same conclusion.\n\nFinally to resolve the problem, they suggest that before training the detection methods, the data be pre-processed to remove the shortcuts. This method alleviate the problem to some degree--the argue that LLM based methods don't need this, and they are already robust.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n-  The experiments are very detailed and I think should be straightforward to reproduce\n-  The topic is very timely, and can help related practitioners\n-  The method is straightforward and intuitive\n-  The improvements are good\n\n**Weaknesses:**\n\n- It is almost impossible to be able to read the paper without reading the appendix, a lot of material is moved to Appendix, and there are a lot of cases that author refer to the tables and discussions in the appendix (just a quick search in the body of the paper confirms this). This has made me to seriously think that this is not the right paper for a conference, perhaps a journal would be a better fit \n- I don't think that Figure 4 is the right way to calculate authenticity.  Both metrics are very old methods for this and are not used anymore. There are more recent works, like those that measure hallucination (like FactScore or other suitable metrics).\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-06"}