Report on the 8th Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI 2024) at CHIIR 2024
Bibliographic record
Abstract
Conversational Agents are increasingly integrated into our daily routines, assisting us with various tasks, from simple commands such as scheduling events to more complex conversational search interactions. Such conversational search systems are traditionally evaluated with word-overlap metrics such as F1 score and accuracy. The full-day workshop on Search-Oriented Conversational Artificial Intelligence (SCAI) at CHIIR 2024 explored the evaluation of conversational search systems from the user's perspective. This interactive workshop included multiple panel discussions and working groups focused on developing and discussing innovative, user-centered evaluation methods for these systems. This paper, co-authored by both organizers and participants of the workshop, presents a summary of the insights gathered from the panel discussions and working groups. Date : 14 March 2024. Website : https://scai.info/scai-2024/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.171 | 0.102 |
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 source (direct Gemma or distilled Codex), 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".