When AI Makes Things Up: Hallucinations, Factuality, and Critical Verification of Responses

Introduction: Why a Fluid Response Does Not Equate to a True Response

Definition of Hallucinations, Factuality, and Lack of Evidence

Hallucinations are plausible linguistic productions that contain information that is false, incoherent, inconsistent with the context, or lacking verifiable support (Huang et al., 2024; Zhang et al., 2025). Factuality refers to the degree to which a response aligns with established facts, while fidelity refers to its correspondence with a specific source, input, or context (Malin et al., 2025; Wang et al., 2025). A statement can be grammatically correct and, at the same time, not grounded in external evidence. Therefore, fluency is a property of generation, not proof of truth.

Risks of Relying on Unverified Content in Everyday and Professional Contexts

The risk increases when users interpret the confidence level as an indication of accuracy. Problems have been documented in legal, journalistic, medical, educational, and scientific information (Augenstein et al., 2024; Zhang et al., 2025). In the legal field, the models evaluated produced errors regarding legal facts and accepted incorrect premises from users (Dahl et al., 2024). Verification must precede any decision whose cost exceeds the time required to verify the response.

How Hallucinations Occur: Model Limitations, Instructions, and Available Knowledge

Plausible generation of false, incoherent, or fabricated information

Language models generate probable sequences based on learned patterns, but this process does not guarantee that every statement corresponds to reality. Training on large corpora can incorporate false information, biases, contradictions, and outdated content (Tonmoy et al., 2024; Wang et al., 2025). The generated output may deviate from the question, contradict the preceding context, or invent entities, quotes, and specific details (Zhang et al., 2025). The absence of an identifiable internal source makes it difficult to distinguish a correct recollection from a statistically plausible continuation.

A formal explanation holds that a computable model cannot learn all computable functions and, therefore, hallucination is inevitable when it is used as a general-purpose problem solver (Xu et al., 2024). This thesis does not imply that every answer is incorrect, but rather that no general architecture can guarantee universal accuracy. Errors occur more readily when the question requires rare knowledge, unobserved information, or inferences outside the training distribution (Ho et al., 2026). A verbally coherent response may precisely mask this lack of knowledge.

Influence of Training Data, Context, and Prompting Strategies

The result depends on the data, the conversational context, and the wording of the prompt. Ambiguous prompts can lead the model to fill in unstated assumptions, while insufficient contextual information limits the model’s ability to justify its response (Tonmoy et al., 2024). The review on prompting strategies distinguishes between errors sensitive to the instruction and errors attributable to the model’s internal behavior (Anh-Hoang et al., 2025). In prompt-sensitive scenarios, structured instructions—including step-by-step reasoning—reduced hallucinations, though they did not eliminate internal limitations (Anh-Hoang et al., 2025).

Context can also create conflict between documents, instructions, and learned knowledge. Retrieving external information shifts this balance but introduces its own errors when it retrieves irrelevant, incomplete, or incorrect documents (Huang et al., 2024; Y. Zhang et al., 2025). Therefore, a response with references is not automatically reliable: retrieval must be evaluated alongside the selection, interpretation, and attribution of sources. Verification requires examining the entire chain from the question to the final statement.

The Tension Between Responding Accurately and Meeting Expectations of Accuracy

Models are often optimized to produce an answer, even when they should acknowledge uncertainty or refrain from answering. Predictive training favors the most likely outcome, and evaluations based solely on accuracy may reward guesswork over the admission of ignorance (Kalai et al., 2026). This pressure explains why a brief, confident answer does not prove that the system has sufficient evidence. Reliability requires that the model adjust its behavior based on the cost of error.

Detecting Whether a Response Is Reliable: Metrics, Uncertainty, and Verification of Claims

Evaluation of factuality, fidelity, and semantic consistency

Detection must distinguish between several objectives. Factuality asks whether a statement matches external facts; fidelity examines whether it correctly represents a source or input; and semantic consistency checks whether different formulations express the same answer (Malin et al., 2025; Wang et al., 2025). These properties are not interchangeable: a summary may be faithful to the document, yet the document may still contain a factual error. Similarly, multiple answers may be semantically consistent while repeating a falsehood.

Automatic metrics depend on the task, the type of statement, and the reference used. Multiple-choice evaluations may mask attribution errors, omissions, and fabrications present in open-ended generation (Malin et al., 2025). Evaluators based on other models show a high correlation with human judgment in certain fidelity studies, but they remain subject to the same problems of comprehension and knowledge (Malin et al., 2025). Methodological reviews point to limitations in datasets, metrics, and domain coverage (Rahman et al., 2026).

A reliable response, therefore, is not defined by a single score. Each statement should be evaluated based on its verifiability, its contextual dependence, and the harm that an error would cause. In open-ended tasks, an adequate evaluation must also account for abstention, uncertainty, and the quality of cited sources. These conditions shift the measurement from mere textual coincidence toward the correspondence between statements and evidence.

Semantic Entropy and Signs of Uncertainty in Model Responses

Semantic entropy estimates the uncertainty regarding the meaning of responses by grouping different formulations that express the same idea (Farquhar et al., 2024). This approach avoids confusing superficial variation in words with conceptual disagreement. The corresponding study shows that meaning-based estimators can detect a subset of confabulations without requiring task-specific data (Farquhar et al., 2024). Their primary utility lies in indicating when the user should intensify verification, not in certifying that a specific response is true.

Internal uncertainty and external verification address different questions. Low entropy may reflect a stable but incorrect answer, while high entropy may arise from an ambiguous question whose correct answer can be phrased in several ways. Therefore, signs of uncertainty must be combined with source checks, analysis of claims, and contextual review. The detection system works best as a risk filter rather than as a substitute for factual verification.

Integrating hallucination detection with fact-checking

Model-centered detection and text-centered fact-checking have evolved with distinct assumptions and protocols (Su et al., 2025). The former seeks internal signals or generation patterns; the latter breaks down a proposition and compares it with retrieved evidence. UniFact’s results indicate that no single approach dominates in all cases and that both capture complementary dimensions of errors (Su et al., 2025). Hybrid systems perform better than the isolated application of each paradigm in the evaluations described in that study (Su et al., 2025).

This framework allows uncertainty to be treated as a signal for prioritization and evidence as the basis for decision-making. The next step is to apply this procedure to independent sources and review their quality.

How to Verify AI Responses: Source Retrieval, External Verification, and Human Review

Break down the response into verifiable statements and search for independent sources

Verification begins by breaking down the response into atomic statements. Each statement must be classifiable as a fact, interpretation, calculation, prediction, or recommendation, because each category requires a different type of verification. Compound statements often conceal partial errors: a date may be correct, but the added causal relationship may lack support. Fact-checking must separately verify entities, quantities, relationships, conditions, and temporal scope.

Next, it is advisable to seek sources independent of the original response. The fact that several pages reproduce the same text does not constitute corroboration, especially if they all rely on a common source. Verification should prioritize institutional documents, primary articles, legislation, official records, or specialized sources appropriate to the subject area. The literature on model-based fact-checking shows that agents perform better when provided with retrieved context, although their accuracy varies depending on the language of the query and the veracity of the claim (Quelle & Bovet, 2024).

Independence also requires formulating queries that do not uncritically incorporate the model’s conclusion. A practical strategy involves identifying the main entity, the central claim, and a possible alternative formulation. If sources disagree, the result should not obscure the disagreement. It should indicate which proposition is supported, which remains uncertain, and what type of evidence would resolve the discrepancy.

Use of augmented retrieval, tools, and references to substantiate information

Retrieval-augmented generation allows for consulting external knowledge and utilizing it during the production of the response. Its advantage is greatest when the question depends on recent, specialized, or domain-specific information (Rahman et al., 2026; Tonmoy et al., 2024). Tools can also support calculations, document searches, and structured verifications. The response, however, must link each relevant claim to the passage that supports it, rather than simply adding a list of links.

Retrieval does not eliminate hallucinations because each component can fail. The searcher may omit the appropriate source, the retriever may select irrelevant material, and the generator may distort the retrieved content (Y. Zhang et al., 2025). A citation may exist and yet fail to support the exact claim. Fact-checking must ask what the source actually states, what population or time period it covers, and whether the model has unduly broadened its scope.

External evidence can also be misleading or of low quality. Fact-checking frameworks involving agents show promising results, but they report inconsistent accuracy and recommend caution regarding the conditions for success and failure (Quelle & Bovet, 2024). The evaluation must include authorship, date, method, conflicts of interest, traceability, and correspondence between the source and the claim. The apparent authority of a web domain is no substitute for content analysis.

Limitations of Automated Fact-Checking and the Need to Evaluate Source Quality

Automation is suitable for identifying contradictions, flagging unsourced claims, and organizing documents, but it does not, on its own, resolve normative or methodological issues. The system can confirm that a phrase appears in a source without establishing that the source is reliable. It can also accept a fabricated reference, attribute a result to another study, or confuse association with causation. These errors are particularly serious in law, medicine, and politics, where interpretation depends on context.

Human review should focus on the highest-risk claims and on points where the tools disagree. It does not involve accepting or rejecting the entire response, but rather auditing its individual pieces of information. In professional settings, the person in charge must retain the source consulted and document any uncertainties that could not be resolved. In this way, verification becomes a reproducible procedure that is proportionate to the potential harm.

Mitigation and Controversies: What Reduces Hallucinations and What Can Go Wrong During Verification

Preventive strategies, post-validation, and integrated detection and correction designs

Mitigation strategies include more precise instructions, domain-specific tuning, external retrieval, the use of tools, post-verification, and abstention mechanisms (Rahman et al., 2026; Tonmoy et al., 2024). Each technique acts on a different phase and may introduce new errors. Retrieval improves access to knowledge but depends on the quality of the index and document selection (Y. Zhang et al., 2025). Post-validation can detect problematic claims, though it requires a suitable source to assess them.

A systematic review of architectures distinguishes between unsupervised generation, reactive validation, proactive detection and mitigation, and integrated designs (Woesle et al., 2025). In that corpus, 87.5% of the studies used reactive or proactive defenses, while 8.8% implemented integrated safeguards at the architectural level (Woesle et al., 2025). This distribution suggests that research has evaluated many partial solutions but fewer systems that combine generation, detection, and correction. Comparing components requires ablation studies and real-world testing, not just aggregate scores.

The Problem of Encouraging Overconfidence Through Accuracy Evaluations

An evaluation based solely on correct answers may reward guesswork. Incentive analysis argues that infrequent details in the training data lead to inevitable errors and that accuracy metrics may penalize abstention (Kalai et al., 2026). Evaluations should specify how errors are penalized and whether the model adjusts its abstention rate based on the task’s risk (Kalai et al., 2026). A high score does not demonstrate prudence if it is achieved through confident answers to unknown questions.

When algorithmic verification undermines human ability to discern headlines

Automated verification can create a false sense of control. In a pre-registered experiment on political headlines, a model correctly identified most of the false headlines, but the information generated by the system did not improve participants’ discernment (DeVerna et al., 2024). Human verification did lead to improvements in that study, whereas the system’s errors reduced belief in true headlines and increased belief in some false ones (DeVerna et al., 2024). Automation can, therefore, displace judgment without making it more accurate.

Summary of the Evidence and Gaps for Practical Verification Literacy

From the seemingly confident response to a risk-proportional verification protocol

The evidence converges on an operational rule: verbal certainty should be treated as a rhetorical, not an epistemic, property. The user must identify the claims, assess uncertainty, retrieve independent sources, and review the correspondence between each proposition and its evidence. The intensity of this protocol should increase with specificity, novelty, and the cost of error. Semantic entropy can help prioritize unstable responses, but it does not replace external verification (Farquhar et al., 2024).

A minimum protocol should require an appropriate source, a reading of the relevant passage, and an explicit statement of uncertainty when discrepancies persist. For low-risk tasks, a brief verification may suffice; for legal, medical, or financial tasks, the review must involve a competent individual. The literature supports combined detection and fact-checking designs, not complete delegation to a single evaluator (Su et al., 2025). This literacy turns AI into a tool for formulation and search, not an authority.

Gaps in the Literature on Users, Domains, and the Consequences of Inadequate Verification

Gaps remain regarding how users actually verify information, which sources they select, and when they abandon the verification process. Research focuses on multimodal questions and tasks, while software engineering, educational technology, and autonomous systems receive less attention (Woesle et al., 2025). There is also a lack of comprehensive evaluations under everyday usage conditions and studies that measure long-term consequences, not just immediate accuracy. The impact of an incorrect source can vary radically across domains, populations, and levels of experience.

Nor does the literature fully address how to communicate uncertainty without inducing overconfidence. Current metrics have limitations in terms of coverage and validity, and the quality of automated evaluators depends on the domain (Malin et al., 2025; Rahman et al., 2026). Protocols are needed to measure appropriate abstention, understanding of citations, detection of poor-quality sources, and decisions made after verification. Without such measures, technical improvement may coexist with an increase in reckless use.

Conclusion: Use AI as a starting point, not as the final authority

Principles for asking questions, verifying, citing, and acknowledging uncertainty

A well-defined query should specify the context, distinguish between facts and inferences, and request verifiable sources. The response should be broken down into statements, cross-checked against independent documents, and reviewed based on the risk associated with its intended use. Citations only support what the source actually states; they do not turn conjecture into knowledge. When evidence is lacking or conflicting, acknowledging uncertainty is more reliable than filling the gap with a fluid response.

AI can accelerate the formulation of questions, the organization of information, and the initial identification of sources. It cannot assume ultimate responsibility for establishing the truth, especially when its incentives favor guessing and evaluations reward accuracy without reservation (Kalai et al., 2026). Responsible practice involves asking precise questions, verifying every relevant claim, citing pertinent evidence, and retaining human review as the final recourse.

References

• Anh-Hoang, D., Tran, V., & Nguyen, L.-M. (2025). Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior. Frontiers in Artificial Intelligence, 8, 1622292. https://doi.org/10.3389/frai.2025.1622292
• Augenstein, I., Baldwin, T. J., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D. P. A., DiResta, R., Ferrara, E., Hale, S. A., Halevy, A. Y., Hovy, E. H., Ji, H., Menczer, F., Míguez, R., Nakov, P., Scheufele, D. A., Sharma, S., & Zagni, G. (2024). Factuality challenges in the era of large language models and opportunities for fact-checking. *Nature Machine Intelligence*, 6(8), 852–863. https://doi.org/10.1038/s42256-024-00881-z
• Dahl, M., Magesh, V., Süzgün, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. The Journal of Legal Analysis, 16(1), 64–93. https://doi.org/10.1093/jla/laae003
• DeVerna, M., Yan, H. Y., Yang, K.-C., & Menczer, F. (2024). Fact-checking information from large language models can decrease headline discernment. Proceedings of the National Academy of Sciences, 121(50), e2322823121. https://doi.org/10.1073/pnas.2322823121
• Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
• Ho, Z. Y., Liang, S., & Tao, D. (2026). Understanding hallucinations in large visual and language models. ACM Computing Surveys, 58(13), 1–36. https://doi.org/10.1145/3811409
• Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2024). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2), 1–55. https://doi.org/10.1145/3703155
• Kalai, A. T., Nachum, O., Vempala, S., & Zhang, E. (2026). Evaluating large language models for accuracy incentivizes hallucinations. Nature, 653(8116), 1047–1051. https://doi.org/10.1038/s41586-026-10549-w
• Malin, B., Kalganova, T., & Boulgouris, N. V. (2025). A review of faithfulness metrics for hallucination assessment in large language models. IEEE Journal of Selected Topics in Signal Processing, 19(7), 1362–1375. https://doi.org/10.1109/jstsp.2025.3579203
• Quelle, D., & Bovet, A. (2024). The perils and promises of fact-checking with large language models. Frontiers in Artificial Intelligence, 7, 1341697. https://doi.org/10.3389/frai.2024.1341697
• Rahman, S. M. A. U., Islam, Md. A., Alam, Md. M., Zeba, M., Rahman, Md. A., Chowa, S. S., Raiaan, M. A. K., & Azam, S. (2026). From hallucination to truth: a review of fact-checking and factuality evaluation in large language models. Artificial Intelligence Review, 59(2). https://doi.org/10.1007/s10462-025-11454-w
• Su, W., Long, J., Wang, C., Lin, S., Xu, J., Ye, Z., Ai, Q., & Liu, Y. (2025). Toward the unification of hallucination detection and fact verification for large language models [Preprint]. In arXiv (Cornell University). https://doi.org/10.48550/arxiv.2512.02772
• Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024). A comprehensive survey of hallucination mitigation techniques in large language models [Preprint]. In arXiv (Cornell University). https://doi.org/10.48550/arxiv.2401.01313
• Wang, C., Liu, X., Yue, Y., Guo, Q., Hu, X., Tang, X., Zhang, T., Cheng, J., Yao, Y., Hu, X., Qi, Z., Gao, W., Wang, Y., Yang, L., Wang, J., Xie, X., Zhang, Z., & Zhang, Y. (2025). Survey on factuality in large language models. ACM Computing Surveys, 58(1), 1–37. https://doi.org/10.1145/3742420
• Woesle, C., Fischer-Brandies, L., & Buettner, R. (2025). A systematic literature review of hallucinations in large language models. IEEE Access, 13, 148231–148253. https://doi.org/10.1109/access.2025.3601206
• Xu, Z., Jain, S., & Kankanhalli, M. (2024). Hallucination is inevitable: An innate limitation of large language models [Preprint]. In arXiv (Cornell University). https://doi.org/10.48550/arxiv.2401.11817
• Zhang, W., & Zhang, J. (2025). Hallucination mitigation for retrieval-augmented large language models: A review. Mathematics, 13(5), 856. https://doi.org/10.3390/math13050856
• Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., Wang, L., Luu, A. T., Bi, W., Shi, F., & Shi, S. (2025). 🧜Siren’s song in the AI ocean: A survey on hallucination in large language models. Computational Linguistics, 51(4), 1373–1418. https://doi.org/10.1162/coli.a.16