Statistical measures, such as Cohen’s Kappa or the Intraclass Correlation Coefficient (ICC), are often employed to quantify the level of agreement between raters, helping to ensure that findings are objective and reproducible. High inter-rater reliability indicates that the findings or measurements are consistent across different raters, suggesting the results are not due to random chance or subjective biases of individual raters. High reliability also boosts a study’s sensitivity, validity, and replicability, which is why researchers treat reporting reliability evidence as standard practice. This error attenuates correlations, making real relationships harder to detect. For example, people who weigh themselves expect a similar reading each time. A scale that gave a different weight every time, or a tape measure that read a different length on repeat use, would not be reliable.
As those presentations and technical talks wrapped up, waves of attendees would suddenly pour back onto the trade show floor, bringing a noticeable surge of energy and conversation around the booth each time. Alongside industrial operations, we spoke with professionals from wastewater treatment, utilities, landfills, healthcare facilities, and other sectors dealing with many of the same maintenance and equipment challenges. A notification is not reliable until receipt is confirmed, meaning is understood, the next owner is explicit, and there is a response clock. Otherwise, the system has documented activity, not protected continuity. NVivo, ATLAS.ti, MAXQDA and similar tools can organize coding, but software does not make coding reliable automatically.
There are many different models of change and implementation, and you can think about high reliability in those terms. Nevertheless, we caution users that multi-turn conversations can be increasingly unreliable owing to divergent LLM responses. Each instruction in BFCL comes with tool set documentation, a JSON object that specifies the set of available actions (APIs) for the assistant to complete user instructions.
We formalize this conversation-induced performance degradation as the conversation tax. Here, suggesting an incorrect distractor in each turn introduces a probability that the model will incorrectly switch to this suggestion. Compounded over tt turns, this results in an end-to-end accuracy lower than if all options were presented simultaneously. As illustrated in Figure 3 and Table A.1, end-to-end accuracy across this-romance.com all turns falls below the single-shot baseline for the majority of evaluated models (14 of 17 on MedQA; 14 of 17 on JAMA CC; 16 of 17 on MedMCQA; Table A.1).
This behavior has been attributed in part to reinforcement learning from human feedback (RLHF), where, in optimizing models toward helpfulness, they are inadvertently taught to prioritize this helpfulness over factual accuracy. The conference attracts reliability managers, maintenance teams, engineers, utilities professionals, wastewater organizations, healthcare facilities, industrial operations, and solution providers looking to improve maintenance and reliability processes. Whether you’re a clinician, manager, executive, educator, researcher, patient safety professional, or healthcare student, I hope you’ll join me for this first conversation. Communication has traditionally been viewed as a human skill—something individuals perform well or poorly. It’s a collaborative space for clinicians, leaders, researchers, educators, patient safety professionals, and students to challenge assumptions, examine evidence, and explore practical ways to make communication safer, more measurable, and more dependable.
Online Data Collection
- A coefficient may be inappropriate even when the software produces a value.
- Each instruction in BFCL comes with tool set documentation, a JSON object that specifies the set of available actions (APIs) for the assistant to complete user instructions.
- This builds on what Hedge, Powell, and Sumner (2018) call the reliability paradox.
The two qualities are distinct but both crucial to strong measurement procedures. Representative qualitative examples (see Appendix for full results) illustrate the characteristic failure modes in multi-turn settings. These cases reveal how long, information-heavy prompts, topic shifts, and misleading mentions break conversational consistency and gradually erode task reliability. We show the prompts for the sharding process below, using Math as an example task. Double-bracketed terms are placeholders that get replaced with the actual data.Other tasks share the same outline with different exemplars and rules to enforce stable outputs. We refer the readers to the GitHub repository for the exact prompts on other tasks.
O1 Sharding
Content validity refers to the extent to which a psychological instrument accurately and fully reflects all the features of the concept being measured. A valid measurement accurately reflects the underlying concept being studied. The bias rating, demonstrated on the horizontal axis of the Media Bias Chart®️, ranges from most extreme left to middle to most extreme right.
Researchers should not copy a model description without confirming that it corresponds to the study design. In reflexive thematic analysis, phenomenology or other interpretive traditions, forcing coders to produce identical interpretations may conflict with the methodology’s assumptions. Researchers should justify their quality criteria rather than importing quantitative procedures automatically.
Healthcare Operations Need Rhythm, Not Just Headcount
The same test is administered to the same group twice, with a reasonable time interval between tests. The correlation coefficient between the two sets of scores represents the reliability coefficient. A high correlation indicates that individuals maintain their relative positions within the group despite potential overall shifts in performance. The date slot is consistently weakest, reflecting difficulty in temporal tracking. Change in mind conversations are most error-prone (85%), while multiple mention cases are relatively robust (91%). Conversational distractions such as temporal shifts or irrelevant chatter differentially impact reliability in realistic reservation tasks.
To understand how this conversational structure influences reliability, we first establish a baseline for accuracy on isolated binary decisions, representing an LLM’s reliability when weighing two arbitrary hypotheses. As expected, narrowing a larger answer-space to a binary choice between the correct answer and a single distractor yields consistent improvements in accuracy across all models and datasets. Averaged across models, reducing the scope of answer options improved accuracy by 33%, 26%, and 26% on MedQA, MedMCQA, and JAMA CC, respectively (Figure 2). Traditional static benchmarks fail to capture the iterative nature of conversational inquiries.
Reliability can change with population heterogeneity, language and setting. AI tools used for coding, scoring, classification or information extraction should be evaluated as fallible measurement procedures. Researchers may need to assess site effects, equipment equivalence, rater agreement and generalizability across settings.
For assistant utterances, we annotated whether the classified strategy was accurate (Strategy Accuracy). For example, if the response is labeled as a clarification, we confirm if it poses a clarification question to the user. When assistant utterances were labeled as answer attempts, we further labeled whether the answer extraction step was successful (Extraction Success). Given an original fully-specified instruction (left-most column in Figure 7), the LLM is prompted to extract segments of the instructions.
The lack of conviction observed across these dialogues introduces critical risks to patient safety. We show that a user’s repeated exploration of alternative concerns systematically degrades the model’s conviction in its initial stance, as well as its ability to recognize a correct hypothesis when presented. Consequently, these models can indiscriminately validate incorrect self-diagnoses or conversely, reinforce medical misconceptions.
In these underspecified regimes, LLMs often make premature assumptions that compound over subsequent turns Laban et al. (2025). Moreover, alignment-induced sycophancy has been shown to cause models to abandon correct initial assessments to conform with inaccurate suggestions Sharma et al. (2023). While recent benchmarks evaluate these conversational dynamics by subjecting models to adversarial user pushback Kim et al. (2026), they focus on a model’s resilience against a user disputing a single medical claim. In real-world care, however, users regularly introduce entirely new, competing hypotheses or erroneous alternative diagnoses McMullan et al. (2019); Polya (1945). How LLMs navigate the sequential presentation of these diverse, competing hypotheses, which may warrant abstention, remains uncharacterized. As such, it is unclear whether these systems are appropriate to handle safety-critical conversations.
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At turn 1 (top row), 96% of the cited documents were introduced in the first turn. The missing 4% correspond to hallucinated citation to documents that were not introduced, and explains why none of the rows’ distribution sum to 100%. At turn two (second row from the top), summaries include citation in roughly equal proportion for turn-1 and turn-2 documents (i.e., 48% and 49%). We rely on a semi-automatic process to transform fully-specified instructions into their sharded equivalents. The process – illustrated in Figure 7 – consists of a sequence of three automated steps (Segmentation, Rephrasing, Verification) followed by a manual step that was conducted by an author of the paper.
Reliability and validity are powerful tools for judging research, but neither is a fixed, one-off box to tick. Transferability involves providing rich descriptions of the research context to allow readers to determine the applicability of the findings to other settings. Sijtsma (2009) showed that alpha is best read as a lower-bound estimate of reliability, one that assumes every item measures the trait with equal precision. Interrater reliability assesses the consistency or agreement among judgments made by different raters or observers. In simpler terms, a reliable tool produces consistent results when applied repeatedly under the same conditions.