Advancing Open-Domain Conversational Agents - Designing an Engaging System for Natural Multi-Turn Dialogue
Bibliographic record
Abstract
This system paper describes our conversational AI agent developed for the SCI-CHAT competition.The goal is to build automated dialogue agents that can have natural, coherent conversations with humans over multiple turns.Our model is based on fine-tuning the Snorkel-Mistral-PairRM-DPO language model on podcast conversation transcripts.This allows the model to leverage Snorkel-Mistral-PairRM-DPO's linguistic knowledge while adapting it for multi-turn dialogue modeling using LoRA.During evaluation, human judges will converse with the agent on specified topics and provide ratings on response quality.Our system aims to demonstrate how large pretrained language models, when properly adapted and evaluated, can effectively converse on open-ended topics spanning multiple turns.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".