A Weak Supervision-Based Approach to Improve Chatbots for Code Repositories
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
Software chatbots are growing in popularity and have been increasingly used in software projects due to their benefits in saving time, cost, and effort. At the core of every chatbot is a Natural Language Understanding (NLU) component that enables chatbots to comprehend the users’ queries. Prior work shows that chatbot practitioners face challenges in training the NLUs because the labeled training data is scarce. Consequently, practitioners resort to user queries to enhance chatbot performance. They annotate these queries and use them for NLU training. However, such training is done manually and prohibitively expensive. Therefore, we propose AlphaBot to automate the query annotation process for SE chatbots. Specifically, we leverage weak supervision to label users’ queries posted to a software repository-based chatbot. To evaluate the impact of using AlphaBot on the NLU’s performance, we conducted a case study using a dataset that comprises 749 queries and 52 intents. The results show that using AlphaBot improves the NLU’s performance in terms of F1-score, with improvements ranging from 0.96% to 35%. Furthermore, our results show that applying more labeling functions improves the NLU’s classification of users’ queries. Our work enables practitioners to focus on their chatbots’ core functionalities rather than annotating users’ queries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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