As a professional concerned with maintaining baselines and auditing for drift in complex systems, I have approached my evaluation of Elicit with a focus on its operational reliability and repeatability. The core promise of the tool—to accelerate the literature review process—is inherently attractive, but my primary concern lies in quantifying the trade-off between efficiency gains and the potential for introducing systematic errors, specifically the omission of seminal or highly relevant papers. A tool that saves 50% of time but misses 15% of key sources represents a significant compliance risk in my field, where due diligence is documented and auditable.
I have conducted a structured, though informal, comparison to measure this trade-off. For a recent project on configuration drift detection in hybrid clouds, I performed two parallel literature searches:
1. A traditional, manual search using a defined protocol across IEEE Xplore, ACM Digital Library, and Google Scholar, utilizing snowballing from known key papers.
2. An Elicit-driven search using the same set of seed questions and a selection of known key papers for the "Cited by" and "References" features.
**Time Measurement Results:**
* **Manual Protocol:** Required approximately 14.5 hours to reach saturation. This included database searching, abstract screening, full-text retrieval, and initial categorization.
* **Elicit-Assisted Protocol:** Required approximately 5 hours. This involved iterative query refinement, reviewing Elicit's synthesized answers, and exporting the paper lists for further examination.
* **Calculated Time Saving:** Roughly 65% reduction in active search and collation time.
**Risk Assessment for Omissions:**
The critical metric. Upon merging the final paper lists from both methods and deduplicating, I analyzed the discrepancies:
* **Elicit-Exclusive Papers:** 22 papers. Most were recent (2022-2023) or from workshops, indicating strength in surfacing newer or niche material.
* **Manual-Exclusive Papers:** 8 papers. This was the high-risk group. Upon analysis:
* 3 were seminal works from the mid-2000s that were highly cited but used terminology slightly different from my modern query phrases.
* 2 were from non-computer-science journals (focusing on compliance), which Elicit's default "Computer Science" filter may have deprioritized.
* 3 were from non-English language sources that had English abstracts, which the manual search in Google Scholar had captured.
This yields a **key paper omission rate of 12.5%** for this specific topic, based on my manual list as the provisional baseline. The risk profile is therefore clear: while the time savings are substantial and the tool excels at broadening the view with recent literature, it can systematically exclude older foundational work and cross-disciplinary perspectives if the query strategy and filters are not meticulously managed.
My open questions to the community are methodological:
* Has anyone else attempted a similar comparative baseline analysis, and if so, what was your framework for defining a "key paper"?
* What specific query construction strategies or workflow steps have you found most effective for mitigating the omission of seminal works? For instance, do you run iterative queries using the terminology from different eras?
* How do you document your Elicit search parameters (seed questions, filters used, date of search) to ensure the review process is reproducible and auditable, akin to a configuration log?