Bibliographic record
Abstract
Along with the broad deployment of deep learning (DL) systems, their lack of trustworthiness, such as their lack of robustness, fairness, and numerical reliability, is raising serious social concerns, especially in safety-critical scenarios such as autonomous driving and aircraft navigation. Hence, a rigorous and accurate evaluation of the trustworthiness of DL systems is essential and would be a prerequisite for improving DL trustworthiness. The first part of the talk will be an overview of certified methods for DL trustworthiness. These methods provide computable guarantees for DL systems in terms of worst-case trustworthiness under certain realistic conditions, such as the accuracy lower bound against arbitrary tiny perturbations. Based on our taxonomy and systematization, we illustrate key methodologies, specifically semantic randomized smoothing and branch-and-bound, and their implications for certified DL trustworthiness. As a representative of recent DL breakthroughs, large language models (LLMs) are transforming our lives, but, on the other hand, posing more challenges to trustworthiness. For example, LLMs can be jailbroken with adversarial prompts to output harmful content with bias, harassment, misinformation, and more. The second part of the talk will be an overview of LLM trustworthiness. We will start with sharing hands-on experience in developing fontier LLMs, then illustrate common LLM trustworthiness issues via examples, then demonstrate evaluation challenges, take one benchmark as an example, and conclude by envisioning certifiable trustworthiness for LLMs.
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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 | 0.035 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".