A Faceted Approach to Language in OLab scenarios
Bibliographic record
Abstract
How is the approach we are taking with conversational agents in OLab different from ChatGPT? Over the past decade, we have been exploring a variety of different approaches for incorporating natural language understanding into OLab.(1–3) Indeed, there is a long history in virtual patients of trying to introduce natural language. Our stance is that, while this is apparently engaging (and cute) at first sight, there are generally only a few areas in any given scenario where constructed responses are important. (https://olab.ca/constructed-responses-in-olab/ ) Our work with TTalk since 2013 has shown just what can be done with a simple chat-based interface, linked to the powerful virtual scenario engine in OLab. This has been shown to be cost-effective, scalable, extensible and with high learning impacts. But it does depend on a human element to a degree, which is both a strength and a limitation. More recently in our DFlow-related work, we have been incorporating more intelligent conversational agents in a manner that is limited in both scope and risk. And given the most recent developments with Microsoft’s AI Bing and ChatGPT, we are glad we have been cautious.(4) It would have been disastrous to unleash an unfettered ChatGPT in certain high risk scenarios. Part of what has made OLab and TTalk so effective in the past ten years is our success in creating scenarios that present a safe space, or more accurately a brave space (somewhere you can be brave enough to try new things), that shields learners from toxic risks and outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".