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Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight

Xinyu Fu, Narayan Ramasubbu, Dennis Galletta

arXiv:2609.01976v1cs.HCcs.AI

TL;DR

LLM errors can pass human review, while prior work has emphasized users’ capability or engagement rather than whether oversight-relevant information is retrievable during review. Across two randomized lab-in-the-field experiments, the paper tests self-generated explanations and retrieval cues, finding improved error detection and more sustained detection under repeated use.

  • Problem

    Prior research has emphasized users’ capability and engagement, leaving information retrievability as an alternative account of effective LLM error detection to examine.

    Source: Introduction · pp. 4–5

  • Method

    Two randomized experiments examine whether self-generated explanations encode verification reasoning and whether matching retrieval cues reactivate it during LLM oversight.

    Source: Introduction · p. 5

  • Results

    Self-generated explanations improved error detection and elaborated recall, while daily retrieval cues slowed the decline in error detection under repeated use.

    Source: Introduction · p. 5

  • Takeaways & Limitations

    Lightweight onboarding self-explanations and daily retrieval cues may make oversight more resilient as LLM use becomes routine.

    Source: Introduction · pp. 5–6

    The paper focuses on user-side conditions and does not examine artifact or organizational conditions such as diagnostic evidence or users’ authority to intervene.

    Source: Retrieval of Oversight-Relevant… · p. 9

Abstract

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Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two randomized lab-in-the-field experiments with 640 customer-facing employees, we show that self-generated explanations improve error detection and strengthen recall of verification-relevant reasoning, while cues that reactivate such reasoning help sustain detection under repeated LLM use. Theoretically, we identify information retrievability as a distinct precondition for effective oversight and specify generative encoding and cue-supported reactivation as mechanisms that build and sustain it. Practically, lightweight onboarding self-explanations and daily retrieval cues can make human oversight more resilient as LLM use becomes routine.

Introduction

The paper argues that oversight can fail not only because users lack capability or motivation, but because verification-relevant information is not retrievable during review. Across two randomized lab-in-the-field experiments, it examines self-generated explanations and cues linked to prior reasoning as ways to improve and sustain error detection.

Contribution

  • A brief cue associated with earlier reasoning can reactivate specific verification checks, offering an alternative to repeated warnings or task-specific corrective information.The cue is intended to restore checks and strategies called for by the earlier error without repeating the original explanation.Source: Introduction · pp. 5–6

Study 1

  • Approximately 10 percentage points: self-explanation improved subsequent error detection versus receiving the explanation without first generating one.In Study 1 (N = 400), participants who generated their own explanation before receiving a standardized explanation also showed more elaborated recall.Source: Introduction · p. 5

Study 2

  • Error detection declined significantly more slowly when the daily message carried the participant’s brief, self-chosen cue tied to earlier reasoning than when it carried an equally personalized but verification-neutral phrase.Together with cue-evocation and cue–episode association measures, this trajectory difference was consistent with the cue reactivating the participant’s earlier reasoning rather than functioning only as a personalized message.Source: Introduction · p. 5

Contribution

  • Oversight can fail despite users’ capability and motivation when review does not cue retrieval of intact verification-relevant reasoning.The authors identify retrievability as a distinct, complementary determinant of effective LLM oversight.Source: Introduction · pp. 5–6
  • Generating explanations produces an elaborated, personally organized representation that remains more accessible in later review than equivalent information received passively.The authors frame this as active encoding of verification-relevant reasoning, rather than exposure to oversight information alone.Source: Introduction · p. 6

Literature Review … Retrieval of Oversight-Relevant Information

Capability- and engagement-based accounts address whether users possess evaluative resources and actively review LLM output, but leave implicit whether oversight-relevant information is accessible at review. The section identifies retrievability as this distinct, designable user-side condition of effective oversight.

Literature Review

  • Effective oversight centers on calibrated error detection—accepting accurate content and intervening on errors—yet errors can survive review even after users learn systems make mistakes.Oversight need not mean generalized skepticism or blanket rejection of LLM output.Source: Literature Review · p. 6

The User-Capability Explanation

  • Capability-based accounts locate oversight failure in inadequate evaluative resources, including system understanding, domain expertise, reference evidence, and verification procedures.Interventions such as AI-literacy training, limitation disclosures, performance or uncertainty information, and system explanations can supply or correct resources, with uneven benefits.Source: The User-Capability Explanation · p. 7
  • Capability accounts explain whether users possess oversight-relevant information, but not whether they undertake and sustain the activity needed to apply it.Such information includes awareness of system errors and boundaries, task knowledge, and verification strategies.Source: The User-Capability Explanation · p. 7

The Engagement Explanation

  • Engagement accounts explain whether users initiate and sustain scrutiny, as automation bias can substitute system output for their own processing and repeated monitoring can wane.Fluent, mostly correct GenAI drafts make exhaustive verification costly and routine acceptance attractive [7,37].Source: The Engagement Explanation · p. 8

Retrieval of Oversight-Relevant Information

  • Neither possession-focused nor engagement-focused accounts explain whether the information required for a given output is accessible during review.When retained information is not retrieved, an intervention may appear ineffective, and a weak or null effect may reflect retrieval failure rather than deficient information or unwillingness to review.Source: The Engagement Explanation · p. 8, Retrieval of Oversight-Relevant… · p. 9
  • Retrievability is whether information needed to verify a particular GenAI output becomes accessible when it is reviewed, a condition distinct from possessing it or actively scrutinizing the output.Users may retain relevant information and still fail to bring it to mind; knowing verification is warranted does not ensure the specific information needed is accessible.Source: Retrieval of Oversight-Relevant… · pp. 8–9
  • The paper develops a retrieval-based account in which encoding oversight-relevant information and cues available during review shape its retrievability.The studies examine recently acquired, task-specific oversight reasoning; the account focuses on user-side conditions, while noting that oversight may also depend on diagnostic evidence and authority to intervene.Source: Retrieval of Oversight-Relevant… · p. 10

Theory and Hypotheses · A Retrieval-Based Account of Oversight · Generative Encoding of Oversight-Relevant Information

The paper argues that effective LLM oversight depends not only on users possessing oversight-relevant information, but on whether it is retrievable and accessible during review. It proposes generative encoding and review-time cues as complementary ways to support that access, and predicts that generating an explanation of an LLM error improves later error detection.

Theory and Hypotheses

  • The framework distinguishes encoding, retrievability, and retrieval: initial encoding shapes potential later access, while cues available at review support retrieval.It applies these relationships to generative encoding and cue-supported reactivation.Source: Theory and Hypotheses · p. 10

A Retrieval-Based Account of Oversight

  • Information can remain in memory yet be inaccessible during review; only retrieved information can directly guide verification of the current LLM output.The paper uses retrievability for the potential for later access and retrieval for the act of accessing information during review.Source: A Retrieval-Based Account of Oversight · pp. 10–11
  • Later access depends jointly on the representation formed during encoding and review-time cues: generative encoding can increase retrievability, while associated cues can support retrieval.A salient reminder may attract attention without reinstating the information needed for verification; an associated cue can reconnect review to an encoded representation [36,31].Source: A Retrieval-Based Account of Oversight · p. 11

Generative Encoding of Oversight-Relevant Information

  • Self-explanation has users identify an error, connect it to reference information, and articulate why the output is incorrect, producing verification-relevant reasoning.This elaborated, self-organized representation is a specific form of oversight-relevant information; more elaborated and organized representations generally support later access better than surface familiarity alone.Source: A Retrieval-Based Account of Oversight · p. 11, Generative Encoding of… · p. 12
  • Generated reasoning should be more retrievable than the same information received passively and can guide error-pattern identification, strategy recall, and claim checking against references.It need only become accessible enough to guide verification, not be consciously or verbatim recalled.Source: Generative Encoding of… · p. 12
  • H1 predicts that users who first generate their own explanation of an LLM error will detect more errors in subsequent output than users given the same standardized explanation without generating one.The proposed mechanism is that generative encoding makes verification-relevant reasoning more retrievable during subsequent review.Source: Generative Encoding of… · p. 13

Cue-Supported Reactivation Under Repeated Use · Participants and Experiment Procedures

The section argues that cues linked to users’ prior verification reasoning can reactivate it during repeated LLM use, and describes two studies testing retrieval support in information-verification tasks.

Cue-Supported Reactivation Under Repeated Use

  • Repeated work can leave reasoning retained but inaccessible as time and intervening tasks accumulate; associated cues can restore access, and repeated retrieval can strengthen later access.These retrieval failures can coexist with routine, habituation, and complacency that reduce scrutiny of system output.Source: Cue-Supported Reactivation Under… · pp. 13–14
  • A cue linked to prior verification reasoning can reinstate that reasoning at review without restating the explanation or supplying task-specific corrective information.The retrieval account distinguishes association with encoded reasoning from a cue’s prominence or informational content.Source: Cue-Supported Reactivation Under… · pp. 13–14
  • Unlike a generic vigilance increase, the retrieval account predicts an advantage for a cue associated with prior reasoning over an equally prominent, unassociated reminder.Habituation or complacency may predict temporary scrutiny from any prominent reminder, but not this association-specific difference.Source: Cue-Supported Reactivation Under… · p. 14
  • H2 predicts that error detection will decline more slowly with cues associated with prior verification reasoning than with comparably presented personalized messages lacking that association.The predicted advantage is longitudinal and should slow, rather than eliminate, declining accessibility.Source: Cue-Supported Reactivation Under… · p. 14

Participants and Experiment Procedures

  • Participants from the same business unit completed separate, non-overlapping studies in December 2025; participation was voluntary, data anonymized, and procedures received IRB approval.Both studies used searchable authoritative references and fluent, professional-looking drafts to assess whether LLM-generated content could be relied on before use.Source: Participants and Experiment Procedures · p. 16
  • Study 1 tested 10 email-verification tasks in one session and varied initial encoding, whereas Study 2 interspersed test emails with routine work over seven workdays and varied retrieval support.The studies shared error categories, searchable references, a review interface, and blind double-coded ground truth.Source: Participants and Experiment Procedures · pp. 16–17
  • Researchers embedded recurring errors found in the organization’s LLM outputs into otherwise accurate drafts, using one predefined error per Study 1 email and potentially multiple errors per Study 2 email.Errors covered applicability or condition, constraint or exception, and procedural inaccuracies; reference materials required selective verification against the drafts.Source: Participants and Experiment Procedures · pp. 16–17

Measurements · Two-Study Mechanism-Testing Strategy

The studies measure error detection against predefined errors and use a coordinated experimental strategy to test whether interventions shape initial encoding and later retrieval support. Proximal process indicators and behavioral outcomes are treated as convergent evidence for the retrieval mechanism, not as direct measures or formal mediators.

Measurements

  • Behavioral error detection is the primary outcome: success in resolving predefined errors is scored on a 0–1 scale at the participant level in Study 1 and participant-day level in Study 2.Study 1 uses partial credit for its single error per email; Study 2 uses binary per-error coding and the proportion corrected for multiple errors per email.Source: Measurements · p. 18
  • Blind independent coders achieved Cohen’s κ = .92 in Study 1 and κ = .89 in Study 2, with discrepancies reviewed and unresolved cases adjudicated by a third blind coder.Scoring criteria and ground truth were specified before data collection.Source: Measurements · pp. 18–19
  • False positives capture altered correct statements; the robustness net-detection index contrasts error-level detection with statement-level false-positive rates and is interpreted as a summary index.Each test email contained 10–13 verifiable correct statements, and the daily false-positive measure in Study 2 follows the same statement-level definition.Source: Measurements · p. 19
  • Both studies recorded verification activity as parallel outcomes, while capability and oversight-orientation covariates were measured before treatment exposure for randomization checks and adjustment.Verification indicators included review time, reference-material consultation, and changes before submission; covariates included AI knowledge, domain capability, and oversight orientation.Source: Measurements · pp. 19–20

Two-Study Mechanism-Testing Strategy

  • Because retrievability is a latent, context-dependent property without a valid situated measure, the authors use coordinated causal-chain experiments rather than conventional mediation analysis.The strategy targets distinct points in the process, examines proximal responses and downstream outcomes, and avoids relying solely on mediator–outcome associations.Source: Two-Study Mechanism-Testing Strategy · p. 21
  • Study 1 varies generative versus system-provided explanation while holding later retrieval support constant; Study 2 holds generative encoding constant and varies a cue linked to prior reasoning.The studies test whether generative encoding improves pre-outcome recall and error detection (H1), and whether cues evoke prior reasoning and slow detection decline with repeated use (H2).Source: Two-Study Mechanism-Testing Strategy · pp. 21–22
  • The studies test complementary links in the process: encoding oversight-relevant information in a more retrievable form and sustaining later access through retrieval support.Study 2 varies whether participants receive a cue associated with their prior reasoning.Source: Two-Study Mechanism-Testing Strategy · p. 22
  • Coordinated interventions affected their intended parts of the process; manipulation-linked process indicators (not direct measures of retrievability) and behavioral outcomes provide convergent evidence for the retrieval mechanism.Source: Two-Study Mechanism-Testing Strategy · p. 22

Study 1: Generative Encoding and Subsequent Error Detection … Study-Specific Measures

Study 1 randomized employees to self-explanation or system-provided explanations, then assessed error detection in unaided review tasks. The study also measured recognition and recall of the illustrative errors and scored detection across ten tasks.

Participants and Design

  • The final analytic sample included 400 employees, with 200 randomly assigned to each explanation condition.Of 407 initial participants, seven were excluded after prespecified attention checks.Source: Participants and Design · p. 22

Experimental Procedure

  • Both groups received the same examples, standardized error explanations, and training time; only the training activity differed.The self-explanation group generated verification-relevant reasoning before feedback, while the comparison group completed a placebo writing task and received feedback directly.Source: Experimental Procedure · p. 23
  • In unaided review, participants completed ten new verification tasks without access to the examples, reference materials, or explanatory content.Source: Experimental Procedure · pp. 23–24

Study-Specific Measures

  • The primary error-detection outcome averaged scores across ten tasks, scoring each predefined error as resolved (1), partly resolved (0.5), or unresolved (0).False-positive and verification measures were aggregated analogously.Source: Study-Specific Measures · p. 24
  • Measures included a condition-recognition manipulation check and recognition of which focal error categories appeared in the examples.The category measure included three focal and three distractor categories.Source: Study-Specific Measures · p. 24
  • Two condition-blind coders rated open-ended error recall on a four-point elaboration scale, with high interrater reliability (Cohen’s κ = .82; linear-weighted κ = .86).Source: Study-Specific Measures · p. 24
  • Study 1 also included a supplementary two-item objective AI-knowledge test, with the application-domain item serving as an objective AI-knowledge covariate.Source: Study-Specific Measures · pp. 24–25

Data Analysis · Results · Randomization and Manipulation Checks

Self-explanation improved Study 1 error detection, with recall and behavioral findings consistent with more accessible verification reasoning and more deliberate review. Randomization and manipulation checks found balanced groups and high, similar recognition of assigned explanation conditions.

Data Analysis

  • H1 was tested with two-tailed regressions of error-detection rates on self-explanation, first unadjusted and then adjusted for AI knowledge, domain capability, and oversight orientation.The condition effect was reported as coefficient b, equal to the mean error-detection-rate difference, with p-values; additional ANOVA, ANCOVA, logistic, and robustness results appear in Online Appendix D.Source: Data Analysis · p. 25

Randomization and Manipulation Checks

  • The conditions showed no significant differences in age, tenure, objective AI knowledge, domain capability, oversight orientation, or gender, and 94.5% versus 91.0% correctly recognized their assigned explanation (χ²(1, N = 400) = 1.82, p = .18).Source: Randomization and Manipulation Checks · p. 25
  • Self-explanation participants spent more time reviewing drafts, consulted references more often, and revised more extensively, yet had a lower false-positive rate (0.09 versus 0.12).These behaviors are consistent with more deliberate, discriminating verification rather than indiscriminate alteration of correct content.Source: Table 4 · p. 26, Randomization and Manipulation Checks · p. 28
  • In the covariate-adjusted model, self-explanation reduced the false-positive rate by three percentage points (b = −0.030, p < .001) and yielded an even larger net-detection advantage.The result was also robust to dichotomizing error detection at the sample median; participants in the self-explanation condition remained more likely to achieve high detection (odds ratio ≈ 3.9).Source: Randomization and Manipulation Checks · p. 28
  • Both conditions ultimately saw the same highlighted errors and standardized explanations; only the work before the explanation differed, consistent with generative encoding but not uniquely diagnostic of retrieval.The authors describe heightened attention and effort as expected downstream expressions of the retrieval account, while noting that Study 2 offers a sharper mechanism test.Source: Randomization and Manipulation Checks · pp. 28–29

Study 2: Sustaining Error Detection Under Repeated Use … Embedded Simulated Task Period (Days 1–7)

Study 2 examined whether retrieval cues could sustain error detection under repeated LLM use, using a shared self-explanation exercise followed by a randomized, seven-workday embedded task period.

Participants and Design

  • Of 254 eligible employees, 253 completed Study 2 (127 non-cue, 126 retrieval-cue); 13 failing prespecified attention checks (7 and 6, respectively) were excluded, leaving 240 analyzed (120 per condition).Source: Participants and Design · p. 29

Experimental Procedure

  • Study 2 consisted of an in-person training session followed by a seven-workday embedded task period.The task period was conducted within employees’ work routines.Source: Experimental Procedure · p. 30

Training Session and Baseline (Day 0)

  • All participants completed Study 1’s self-explanation exercise and a common baseline assessment before retrieval cues were generated or shown.Cues were first displayed at login on Day 1, making Day 0 a pre-treatment baseline; Study 2 had no parallel system-provided-explanation arm.Source: Training Session and Baseline (Day 0) · pp. 30–31

Embedded Simulated Task Period (Days 1–7)

  • Each workday, employees received their usual customer inquiries with three simulated test emails interleaved to approximate routine work conditions.Test-email timing, frequency, and content were not disclosed to participants.Source: Embedded Simulated Task Period (Days… · p. 31
  • Retrieval-cue participants generated a brief phrase tied to their training realization about how an LLM response could be wrong, intended to reactivate that reasoning.They were instructed not to use a generic warning or verification reminder.Source: Embedded Simulated Task Period (Days… · p. 31
  • The retrieval cue appeared once per workday in the standard login welcome message, as a lightweight peripheral element rather than a task-level prompt.Participants encountered it at the start of the workday, not after each task.Source: Embedded Simulated Task Period (Days… · p. 32
  • The non-cue group personalized the same welcome message with a cosmetic phrase, matching customization and presentation while varying its connection to the earlier reasoning episode.Interface elements, message timing, and LLM drafting behavior were identical across conditions.Source: Embedded Simulated Task Period (Days… · p. 32

Measures … Descriptive Trajectories of Error Detection and Verification Behavior

Without retrieval cues, error detection and verification effort declined over repeated LLM use; with self-generated cues, both behaviors were better sustained. The study measured email-level error correction and tested whether detection changed more slowly in the cue condition.

Measures

  • An email’s error-detection rate was the proportion of predefined errors corrected, with the most consequential error designated by domain experts for compliance or financial risk.Auxiliary verification measures and daily false-positive rates were aggregated to the participant-day level.Source: Measures · pp. 32–33

Data Analysis

  • The analysis aggregated 5,760 email observations into 1,920 participant-day observations from 240 participants and tested H2 with mixed-effects models of day, cue, and their interaction.The day × cue interaction tests whether detection declined more slowly with cues; cue-evocation measures served as convergent process evidence rather than individual-level mediation.Source: Data Analysis · p. 33
  • Robustness analyses examined alternative error-detection outcomes and model specifications, including false positives, net detection, and correction of the most consequential errors.The design’s estimated 80%-power threshold for the day × cue interaction was b ≈ 0.009 per day, comparable in magnitude to the observed coefficient.Source: Data Analysis · pp. 33–34

Randomization and Manipulation Checks

  • Conditions were balanced at Day 0 on error detection and measured participant characteristics, while cue-condition verification took about three seconds longer before cue generation and display.The baseline verification-time difference was attributed to chance; Day 0-adjusted models retained a positive, similar-magnitude but only marginally significant interaction.Source: Randomization and Manipulation Checks · p. 34
  • Most participants recognized their own welcome phrase: 86.7% in the retrieval-cue condition and 80.0% in the non-cue condition, a nonsignificant difference.The exposure check indicated that participants attended to the daily message.Source: Randomization and Manipulation Checks · p. 35

Descriptive Trajectories of Error Detection and Verification Behavior

  • At Day 7, mean error detection was 0.445 in the non-cue condition and 0.517 in the retrieval-cue condition, after comparable Day 0 performance.The widening gap reflects differences in trajectories rather than the small baseline difference.Source: Descriptive Trajectories of Error… · pp. 35–36
  • Without cues, participants progressively spent less time reviewing LLM responses and consulted references less often; self-generated cues substantially reduced these declines.Some decline remained in the cue condition; day-specific task composition may contribute to the shared temporal trend, but the conditions shared each day’s task set.Source: Descriptive Trajectories of Error… · pp. 35–36

Model-Based Test of H2 · Process Evidence for the Retrieval Mechanism

In Study 2, retrieval cues slowed the decline in error detection and evoked verification-relevant reasoning. Together with Study 1’s encoding findings, the results support initial encoding and subsequent retrieval support as components of the retrieval process.

Model-Based Test of H2

  • The day × cue interaction was positive and significant (b = 0.008, p = .013), with detection declining less per day in the cue condition (−0.017) than without cues (−0.025).The primary specification supports H2: the cue slowed the subsequent decline rather than producing only a one-time boost.Source: Table 7 · p. 37, Model-Based Test of H2 · p. 37
  • By Day 7, the cue corresponded to catching about half an additional embedded error, offsetting about one third of daily erosion.The estimated cumulative detection-rate difference was 0.008 × 7 ≈ 0.056; each day’s three test emails contained nine embedded errors on average.Source: Model-Based Test of H2 · p. 37
  • The cue did not change false-positive drift (b = 0.070 × 10−3, p = .93), while its interaction remained significant for net detection (b = 0.008, p = .02).Source: Model-Based Test of H2 · p. 38
  • For the expert-designated most consequential errors, detection eroded by b = −0.101 per day, and the cue effect was largest (b = 0.021, p < .001).Source: Model-Based Test of H2 · p. 38

Process Evidence for the Retrieval Mechanism

  • Cue recipients more often showed a verification mindset (77.5% versus 48.3%) and had higher evocation ratings (Mean = 2.15 versus 1.36).The evocation-rating comparison was statistically significant (t(238) = 6.85, p < .001).Source: Process Evidence for the Retrieval… · p. 39
  • Cue–episode association was higher with retrieval cues (Mean = 2.86 versus 2.44), despite both conditions receiving equally personalized messages through the same channel.This contrast tests deliberate association with prior verification reasoning rather than cosmetic customization.Source: Process Evidence for the Retrieval… · pp. 39–40
  • Evocation was a manipulation-linked process indicator, not an individual-level mediation test or direct measure of retrievability.It was collected only at study end, after repeated outcomes, and had limited within-condition variation.Source: Process Evidence for the Retrieval… · p. 40
  • Study 1 showed generative encoding improved recall and error detection, while Study 2 found associated cues helped sustain access and slow detection erosion during repeated use.Together, the studies provide coordinated evidence for establishing retrievability through initial encoding and sustaining access through retrieval support.Source: Process Evidence for the Retrieval… · p. 41

Robustness Checks

Robustness checks show that the positive day × cue interaction persists across alternative model specifications and outcome measures. The checks also weigh against explanations based on generalized skepticism, cue content alone, or generic message attributes.

Robustness Checks

  • The day × cue interaction remained positive and statistically significant across alternative specifications and outcome operationalizations, though baseline adjustment made it less precise.It remained significant in random-slope, day-fixed-effects, email-level, error-level, and net-detection models; adding Day 0 performance as a baseline covariate over Days 1–7 left the interaction positive but less precisely estimated.Source: Robustness Checks · p. 41
  • Detection of the expert-designated highest-risk error declined most steeply and showed the largest cue effect, while median-dichotomized detection yielded the same substantive conclusion.The highest-risk error was assessed in each email; median-dichotomized error detection is reported in Online Appendix D.Source: Robustness Checks · p. 41
  • False-positive rates were nearly identical across conditions (0.117 versus 0.116), with no cue-induced increase, consistent with sustained detection of genuine errors rather than generalized skepticism.The net-detection measure reproduced the focal pattern.Source: Robustness Checks · pp. 41–42
  • The day × retrieval-cue interaction remained positive and significant after excluding three noncompliant generic cues, while ordinary-semantic cues showed no detectable slope advantage over idiosyncratic cues.Among 117 instruction-compliant cue users, the idiosyncratic subgroup had a flatter estimated trajectory than the non-cue condition, and its slope did not detectably differ from ordinary-semantic cues.Source: Robustness Checks · p. 42
  • Conditions did not differ significantly in measured message attributes (t(238) = 1.34, p = .18), and those attributes did not predict error-detection trajectories.The non-cue condition received an equally personalized message in the same visual format and channel; the composite included meaningfulness, noticeability, memorability, liking, and attention capture.Source: Robustness Checks · p. 42

Overall Discussion … Sustaining Oversight by Reactivating Prior Reasoning

The discussion argues that effective oversight depends on relevant reasoning being accessible at review, not merely known or reviewed, and that self-generated explanations and linked cues can build and sustain that access.

Overall Discussion

  • Across both studies, generating an explanation supported later access to verification-relevant reasoning, while an associated cue helped reactivate it across repeated use.The discussion presents these findings as support for a retrieval-based account of oversight failure.Source: Overall Discussion · pp. 42–43

Retrievability as a Distinct and Complementary Precondition for Oversight

  • Retrievability is a distinct precondition for oversight because capability and engagement do not ensure relevant reasoning comes to mind during a particular review.The discussion frames retrievability as a condition for translating capability and engagement into effective oversight, rather than simply another input.Source: Retrievability as a Distinct and… · pp. 43–44
  • The retrievability question extends beyond LLM-generated text to any review of automated recommendations, risk scores, or generated content.In each setting, the issue is whether previously encoded verification-relevant reasoning is accessible at the moment of review.Source: Retrievability as a Distinct and… · p. 44

Encoding Oversight-Relevant Information for Later Retrieval

  • What users do while learning information can affect later error detection independently of the information’s content, making encoding a separate target for oversight support.Content determines what information is available; encoding shapes the representation available for later review.Source: Encoding Oversight-Relevant… · p. 45
  • Self-generated explanations can prepare reasoning for transfer to new reviews because explanation construction engages operations resembling those required for review.The broader design principle is to engage users in representational work that prepares verification-relevant reasoning for later retrieval; comparable processing may also serve this function.Source: Encoding Oversight-Relevant… · p. 45

Sustaining Oversight by Reactivating Prior Reasoning

  • Reactivation can sustain error detection as it declines across repeated use by restoring access to reasoning users have already formed.An associated cue can do so without retraining, repeating the original explanation, or supplying task-specific corrective information.Source: Sustaining Oversight by Reactivating… · pp. 45–46
  • A cue works as reactivation when linked to prior verification-relevant reasoning, not merely because it draws attention, and restoring diagnostic reasoning should preserve discrimination between erroneous and acceptable outputs.This distinguishes reactivation from both generic attention increases and blanket skepticism.Source: Sustaining Oversight by Reactivating… · p. 46
  • The discussion treats oversight as something to maintain across repeated encounters, addressing whether prior verification-relevant reasoning remains accessible as system use becomes routine.Declining detection under routine use is framed partly as a retrieval problem to address through reactivation rather than re-instruction.Source: Sustaining Oversight by Reactivating… · pp. 46–47

Practical Implications · Limitations and Future Research · Conclusion

Effective oversight depends partly on whether verification-relevant reasoning can be retrieved at review: self-generated explanations strengthen its encoding, and cues help keep it accessible as LLM use becomes routine. Organizations can support retrieval at onboarding and during review, but it does not remove the time and attention oversight requires; the studies also leave open questions about mechanisms and generalizability.

Practical Implications

  • At onboarding, users can explain why an output is wrong and how to verify it; tools can resurface user-associated cues at review instead of relying only on warnings or disclaimers.The recommendation is to design for retrieval rather than disclosure, so users can reinstate their reasoning without being retaught.Source: Practical Implications · pp. 47–48
  • Retrieval support lowers the informational barrier but does not remove oversight’s cost: review takes time and attention, and added duties without adjusted output targets risk trading caught errors for pace.Organizations should track error detection alongside false positives and review time rather than assuming one training session resolves the challenge.Source: Practical Implications · p. 47

Limitations and Future Research

  • The experiments manipulated retrieval antecedents and recorded behavioral consequences but did not observe whether relevant reasoning was accessible immediately before review.Future work could track retrieval at that moment to add temporal resolution to the account.Source: Limitations and Future Research · p. 48
  • The study’s controls did not fully separate retrieval from the attention or effort recruited by reactivation, leaving those downstream channels for future research.The matched-message control and message-attribute checks addressed personalization and measured salience, memorability, and liking, but not the full attention-or-effort alternative.Source: Limitations and Future Research · p. 48
  • Because the study covered one organization, searchable reference materials, and fixed error types, retrieval support may work differently for expertise-heavy or high-stakes tasks and where references are unavailable.Source: Limitations and Future Research · p. 48
  • Future work should test cue frequency and specificity, relevance after model updates, ways to restore weakening cues, and how retrieval combines with capability and engagement.The authors note that manipulating all three preconditions together may require a laboratory setting where repeated use is not possible.Source: Limitations and Future Research · p. 48
  • Because all Study 2 participants began with the same self-generated explanation, the study could not track a provided-explanation group over time; following both groups would show how the initial encoding gap evolves.Source: Limitations and Future Research · pp. 48–49

Conclusion

  • Self-generated explanations strengthened verification-relevant reasoning, while self-chosen cues helped keep it accessible as LLM use became routine.The conclusion frames information accessibility at the moment of review as part of effective oversight.Source: Conclusion · p. 49

Appendix … Appendix B. Illustration of the Self-Generated Retrieval Cue Mechanism

The appendices distinguish having oversight-relevant information and undertaking review from being able to retrieve the information needed for a particular LLM output. They illustrate how a self-generated cue can reactivate earlier verification reasoning without repeating it or supplying task-specific correction.

Appendix A. Extended Review of Related Literature on Human Oversight of AI Output

  • Generated drafts can be coherent and mostly correct while containing consequential local errors, requiring reviewers to identify claims, locate evidence, and correct errors without discarding valid content.This requires both understanding why fluent output can be wrong and having task-specific means to test claims.Source: Appendix A · pp. 52–53
  • Capability accounts concern whether users possess evaluative resources, and engagement accounts whether they initiate and sustain review; neither explains whether needed information is retrievable during review.A user may possess relevant knowledge and actively scrutinize output yet fail to retrieve the domain knowledge, error pattern, or verification strategy needed for a particular error.Source: Appendix A · pp. 53–54

Capability Perspective

  • Prior studies associate task-based experience with greater AI productivity gains [39], find that inadequate monitoring can lead physicians to accept incorrect advice [18], and condition human improvement of machine predictions on complementary judgment [30].Source: Capability Perspective · p. 55
  • Self-generated explanations improve later unaided error detection, while self-generated retrieval cues slow its decline over repeated use without repeating explanations or supplying task-specific corrections.The table summarizes findings from two lab-in-the-field experiments with organizational users reviewing LLM-generated output.Source: Capability Perspective · p. 56

Appendix Table A1. Related Literature on Human Oversight of AI Output Specific focus or mechanism

  • The literature table groups representative studies by user-side oversight mechanism and distinguishes capability, engagement, and retrievability, while noting that artifact and organizational conditions may also matter.It characterizes retrievability as accessing previously encoded oversight-relevant information at review.Source: Appendix Table A1. Related Literature… · p. 55, Capability Perspective · p. 56

Appendix B. Illustration of the Self-Generated Retrieval Cue Mechanism

  • In the illustration, participants explained how an LLM response might be wrong, then generated a brief phrase tied to their own realization, such as “version trap.”The example explanation identifies outdated product rules and the need to check applicability conditions.Source: Appendix B · p. 57
  • During the seven-day task period, the cue appeared once per workday in a login message to reactivate the earlier realization, without repeating its explanation or providing task-specific corrective information.Cues were peripheral and not linked to a particular task, document, or error category; examples included idiosyncratic labels such as “pink elephant.”Source: Appendix B · p. 57
  • 97.5% of the 120 cues followed the instruction: 43.3% were idiosyncratic personal tags, 54.2% had ordinary meaning, and 2.5% were strict departures.Both conditions received personalized daily messages, but only retrieval-condition messages had been associated with prior verification-relevant reasoning.Source: Appendix B · pp. 57–58

Appendix C. Measures and Materials … Appendix Table E1. Correlation Matrix for Study 1 Variables (N = 400)

The appendices document how measures were constructed, test the stability and interpretation of the treatment effects, and report descriptive correlation matrices. Robustness checks support the Study 1 self-explanation effect and the Study 2 retrieval-cue trajectory effect, with qualifications around baseline adjustment and mechanism evidence.

Appendix C. Measures and Materials

  • Behavioral outcomes were derived from system logs, and all study materials were administered in Chinese, with English provided as an illustrative translation.The outcomes include error detection, false-positive rate, verification time, reference-material consultation, and textual modification.Source: Appendix C · p. 58

Study 2 measures

  • Study 2 measures assessed recognition of participants’ login phrases, cue evocation, cue–episode association, and phrase attributes.Cue–episode association had Cronbach’s α = .81, phrase attributes had α = .91, and phrase recognition accuracy was 86.7% and 80.0% across conditions.Source: Study 2 measures · p. 59

Appendix D. Robustness Tests for Study 1 and Study 2

  • Across alternative outcome codings, the Study 1 self-explanation estimate was b = 0.105, p < .001, and the Study 2 study day × retrieval-cue estimate was b = 0.008, p = .013.These estimates were insensitive to alternative domain-capability and AI-knowledge codings and to omitting covariates; Appendix Table D1 consolidates estimates across outcome codings.Source: Appendix D · p. 60

Robustness Tests for Study 1

  • Study 1 self-explanation reduced false positives (b = −0.030, p < .001) while increasing net detection (b = 0.136, p < .001).The false-positive rate was 0.09 versus 0.12, and the effect remained with demographic controls and binary detection coding (adjusted odds ratio = 3.9, p < .001).Source: Robustness Tests for Study 1 · pp. 60–61

Robustness Tests for Study 2

  • Study 2 checks found no cue-related increase in false positives, while baseline-adjusted and mechanism analyses qualify interpretation of the trajectory effect.The Day 0-adjusted interaction was positive but not conventionally significant (b = 0.007, p = .064); cue-form comparisons were exploratory, and evocation results are convergent process evidence rather than individual-level mediation.Source: Robustness Tests for Study 2 · pp. 61–63
  • Study 2’s day × cue interaction remained positive across random-slope, day-fixed-effects, email-level, and error-level models.The random-slope estimate was b = 0.008, p = .013; the email-level estimate was b = 0.007, p = .018, and the embedded-error odds ratio was 1.03, p = .035. Alternative detection codings also showed positive interactions, including odds ratio = 1.17, p < .001 for high detection.Source: Robustness Tests for Study 2 · pp. 61–62

Appendix E. Correlation Matrices; Appendix Table E1. Correlation Matrix for Study 1 Variables (N = 400)

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