Conversation Alignment for Task-Oriented Dialogue Agents
Bibliographic record
Abstract
Verification is a core component for dialogue agents, as all conversations within scope must be handled predictably. The current approaches used to analyze agent capabilities are time-consuming and tedious, leaving dialogue designers unable to reliably understand the capabilities of their agents. In this paper, we address this issue with our novel method for systematic testing called Conversation Alignment, which uses a tailored Beam Search algorithm to explore how well the agent can handle given conversations. We also provide the dialogue designer with visual metrics that indicate where the majority of conversations are failing. We evaluated our system by measuring how effectively errors are captured, using the system to find errors iteratively, and scaling hyperparameters to test how performance was affected. We show that Beam Search is more effective than Greedy Search in providing useful failure metrics to the dialogue designer and that Conversation Alignment is an effective tool for incrementally reducing the number of failed conversations when used iteratively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".