Bibliographic record
Abstract
As conversational agents (CAs) improve in social communication, interest in making their dialogueflows more intuitive grows. While emulating human-to-human (HxH) conversations is the focus, evidence suggests humans use different strategies with artificial agents. This thesis explores this through two studies. The first examines users’ text-based interactions with a ‘Wizard of Oz’ (WOZ) agent (a human simulating the CA) and a Large Language Model (LLM). The second study investigates the impact of affective states using the EmoWOZ dataset with six emotional labels. Comparing human-to-agent (HxA) interactions to HxH conversations, we analyze linguistic markers of five social dynamics: demonstrating engagement, establishing common context, displaying emotional charge, supporting group cohesion, and applying social protocols. Our findings reveal significant linguistic differences between HxA and HxH interactions, the influence of agent perception on communication strategies, and the role of affective contexts. This research aims to assess user interactions and enhance CAs in navigating diverse communication strategies in HxA interactions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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; both teacher heads agree on what is shown here.
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".