Human perception has the unique ability to focus on specific events in a mixture of signals--a challenging task for existing non-intrusive assessment methods. In this work, we introduce semi-intrusive assessment that emulates human attention by framing audio assessment as a text-prediction task with audio-text inputs. To this end, we extend the multi-modal PENGI model through instruction fine-tuning for MOS and SNR estimation. For MOS, our approach achieves absolute Pearson correlation gains of 0.06 and 0.20 over the re-trained MOSRA model and the pre-trained PAM model, respectively. We further propose a novel SNR estimator that can focus on a specific audio source in a mixture, outperforming a random baseline and the fixed-prompt counterpart. Our findings suggest that semi-intrusive assessment can effectively capture human-like selective listening capabilities. Samples are available at https://jozefcoldenhoff.github.io/semi-intrusive-assessment.