This study applies mechanistic interpretability techniques to uncover and analyze computational circuits within LLMs during biomedical question answering, and employs Automatic Circuit Discovery in combination with Edge Attribution Patching to enable scalable circuit extraction beyond toy settings.
This work presents a scalable, reproducible framework for evaluating, optimizing, and interpreting LLMs for biomedical knowledge extraction, with a focus on gene–gene regulatory relation prediction, pathway component recognition, multimodal pathway figure understanding, and automated prompt optimization.
THGAgents utilizes collaborative and dynamically updating agents to build a Traceable Causal Knowledge Graph, which serves as the foundation for the evidence-based knowledge structure and employs an LLM-driven heuristic search algorithm to traverse the complex network, balancing both novelty and rigor to deduce strict,...
Ming-Jia Yang, Kun-Hua Dong, K. Lim et al.· Proceedings of the Thirty-Fi...· 0 citations
A mechanistic analysis of the jailbreaking behavior in a large-scale, safety-aligned LLM, focusing on LLaMA-2-7B-chat-hf identifies computational circuits responsible for generating affirmative responses to jailbreak prompts and uncovers key attention heads and MLP pathways that mediate adversarial prompt exploitation.
Paria Mehrbod, Boris Knyazev, Guy Wolf et al.· 0 citations
Text content is the dominant factor in annotation decisions, far outweighing annotator demographics, and that content-focused SHAP explanations are more effective than demographic persona prompting for guiding LLM annotations, showing that explainability methods can improve both the reliability and the transparency of...
An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.
A robust technical framework is established for developing trustworthy, high-efficiency medical AI systems capable of operating entirely within hospital-controlled infrastructure by evaluating the synergistic impact of context window scaling and multi-stage supervised fine-tuning within a localized Retrieval-Augmented...
L. Pawlik, Stanisław Deniziak· Scientific Reports· 0 citations
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