However, foundational theoretical questions about this algorithm's privacy loss remain open--even in the seemingly simple setting of smooth convex losses over a bounded domain. Our main result resolves these questions: for a large range of parameters, we characterize the differential privacy up to a constant.

Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by generating them before answering during inference, or by placing them before or after the original answers during training - significantly improves model performance on mathematical, symbolic and commonsense reasoning tasks. However, most work focuses on the role of rationales in these reasoning tasks, overlooking their potential impact on other important tasks like natural language understanding (NLU) tasks. In this work, we raise the question: Can rationales similarly benefit NLU tasks? To conduct a systematic exploration, we construct NLURC, a comprehensive and high-quality NLU dataset collection with rationales, and develop various rationale-augmented methods. Through exploring the applicability of these methods on NLU tasks using the dataset, we uncover several potentially surprising findings: (1) CoT inference shifts from hindering NLU performance to surpassing direct label prediction as model size grows, indicating a positive correlation. (2) Most rationale-augmented training methods perform worse than label-only training, with one specially designed method consistently achieving improvements. (3) LLMs trained with rationales achieve significant performance gains on unseen NLU tasks, rivaling models ten times their size, while delivering interpretability on par with commercial LLMs.

We also provide the ground truth object masks. Question 2: How many instances are there in total of each type?

This work critically examines this explanation.

We answer your main questions as follows. "Is there any hope to avoid the We will add a remark in the paper to discuss this point more thoroughly. Question 2. "Technically, I think in order for Lemma 4 to hold, f needs to be defined on the whole vector space" The issue has also been identified by Reviewer #3. We will improve the paper writing to make this point more clear. Question 2. "what regret ... if ... only access to 1 gradient query per step, rather than the two used in OEGD." We address your main questions as follows. Question 1. "how would the lower-bound of function appear in your bounds if we assume they are not positive" Question 2. "how would the algorithms / results change if 0 is not in X?" Answer 2. There are three places we use this assumption: About the self-bounding property of smooth functions, you are absolutely correct. For other minor issues, we will carefully revise the paper according to your constructive comments. Below we address your concerns and clarify the misunderstandings. Question 2. "The novelty of the paper is limited.

Automating benefit verification phone calls saves time in healthcare and helps patients receive treatment faster. It is critical to obtain highly accurate information in these phone calls, as it can affect a patient's healthcare journey. Given the noise in phone call transcripts, we have a two-stage system that involves a post-call review phase for potentially noisy fields, where human reviewers manually verify the extracted data$\unicode{x2013}$a labor-intensive task. To automate this stage, we introduce Auto Review, which significantly reduces manual effort while maintaining a high bar for accuracy. This system, being highly reliant on call transcripts, suffers a performance bottleneck due to automatic speech recognition (ASR) issues. This problem is further exacerbated by the use of domain-specific jargon in the calls. In this work, we propose a second-stage postprocessing pipeline for accurate information extraction. We improve accuracy by using multiple ASR alternatives and a pseudo-labeling approach that does not require manually corrected transcripts. Experiments with general-purpose large language models and feature-based model pipelines demonstrate substantial improvements in the quality of corrected call transcripts, thereby enhancing the efficiency of Auto Review.