Bibliographic record
Abstract
With the introduction of Large Language Models (LLMS) and their integration with software development tasks, the software development landscape has changed drastically in the last couple of years. In this session, we delve into the intricate world of large language models for code (LLMs4code) and explore their benefits, challenges, and threats. On one hand, these models have revolutionized code completion, bug detection, and even generated entire sections of code with remarkable accuracy. However, on the other side, several concerns have emerged surrounding inaccurate, buggy, and vulnerable code generation, biases, implications for climate, and the potential for unintended consequences. Together, we'll dissect real-world examples, dis-cussing the transformative power of large language models while exploring the gray side of LLMs4code that developers tread. The talk will discuss strategies for effectively leveraging these tools, mitigating risks, and contributing to the ongoing dialogue about responsible AI in the coding ecosystem. The talk promises an exploratory take that not only seeks to harness the potential of LLMs4code but also ensures a conscientious and mindful approach toward their integration into our coding practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".