Clinical ambient documentation systems need to detect dictation from doctor-patient conversations to ensure critical statements are properly recorded. Existing methods struggle to detect dictation interleaved within conversational exchanges. We investigated LLM-based approaches: zero-shot, multi-shot, manual/auto prompt tuning, and supervised fine-tuning for medical dictation detection. A word alignment-based method is proposed to mitigate ASR errors and LLM hallucinations while performing utterance- and word-level evaluation. Supervised finetuning of a Qwen2-1.5B LLM achieves the best performance: $\mathbf{9 0. 4 \%}$ utterance-level and $\mathbf{8 6. 8 \%}$ word-level macro F1 on manual transcripts, with minimal loss on ASR. This surpasses Claude 3.5 Sonnet with auto-tuned prompts and multiple in-context learning examples, though the latter requires much less training data. We release model outputs on the ACI-Bench dataset, providing the first open resource for the evaluation of medical dictation detection.

This paper considers the problem of estimating an unknown high dimensional signal from (typically low-dimensional) noisy linear measurements, where the desired unknown signal is assumed to possess a group-sparse structure, i.e. given a (pre-defined) partition of its entries into groups, only a small number of such groups are non-zero. Assuming the unknown group-sparse signal is generated according to a certain statistical model, we provide guarantees under which it can be efficiently estimated via solving the well-known group Lasso problem. In particular, we demonstrate that the set of indices for non-zero groups of the signal (called the group-level support of the signal) can be exactly recovered by solving the proposed group Lasso problem provided that its constituent non-zero groups are small in number and possess enough energy. Our guarantees rely on the well-conditioning of measurement matrix, which is expressed in terms of the block coherence parameter and can be efficiently computed. Our results are non-asymptotic in nature and therefore applicable to practical scenarios.

Recent years have seen a revival of interest in the Alternating Direction Method of Multipliers (ADMM), due to its simplicity, versatility, and scalability. As a first order method for general convex problems, the rate of convergence of ADMM is O(1=k) [4, 25]. Given the scale of modern data mining problems, an algorithm with similar properties as ADMM but faster convergence rate can make a big difference in real world applications. In this paper, we introduce the Accelerated Alternating Direction Method of Multipliers (A2DM2) which solves problems with the same structure as ADMM. When the objective function is strongly convex, we show that A2DM2 has a O(1=k2) convergence rate. Unlike related existing literature on trying to accelerate ADMM, our analysis does not need any additional restricting assumptions. Through experiments, we show that A2DM2 converges faster than ADMM on a variety of problems. Further, we illustrate the versatility of the general A2DM2 on the problem of learning to rank, where it is shown to be competitive with the state-of-the-art specialized algorithms for the problem on both scalability and accuracy.