The Bot on Speaking Terms: The Effects of Conversation Architecture on Perceptions of Conversational Agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conversational agents mimic natural conversation to interact with users. Since the effectiveness of interactions strongly depends on users’ perception of agents, it is crucial to design agents’ behaviors to provide the intended user perceptions. Research on human-agent and human-human communication suggests that speech specifics are associated with perceptions of communicating parties, but there is a lack of systematic understanding of how speech specifics of agents affect users’ perceptions. To address this gap, we present a framework outlining the relationships between elements of agents’ conversation architecture (dialog strategy, content affectiveness, content style and speech format) and aspects of users’ perception (interaction, ability, sociability and humanness). Synthesized based on literature reviewed from the domains of HCI, NLP and linguistics (n=57), this framework demonstrates both the identified relationships and the areas lacking empirical evidence. We discuss the implications of the framework for conversation design and highlight the inconsistencies with terminology and measurements.
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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 | 0.001 | 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 it