LLMs are reshaping ML research workflows, but the debate is about boundaries: should researchers lean on them for summaries, literature searches, and boilerplate coding, or worry about blurring the line between human insight and machine prose? The discussion also flags how much a model should know and whether we should probe low-level mechanisms or high-level semantics [1].
Reliance on LLMs in research – Proponents say LLMs help with rapid paper summaries, topic overviews, and even debugging boilerplate code, acting like a fast Google and a first-read assistant [1]. The conversation even spots questions about authorship when tools like ChatGPT and Gemini 2.5 Pro are used for substantial parts of work, not just quick queries [1].
Historical knowledge limits in LLMs – A second thread imagines training LLMs only on pre-breakthrough knowledge, then asking for new theory without post-breakthrough data or experimental validation. It calls for strict leakage controls and novelty-focused evaluation, pointing to a Nature study as a basing reference for this line of inquiry [2].
Mechanistic interpretability vs semantic probing – A third angle pits mechanistic interpretability (low-level, weight- and circuit-reading) against semantic probing (high-level, behavior-focused tests of reasoning). The discussion centers on feasibility, impact on skill development, and choosing a path that keeps projects finishable within time and career goals [3].
Closing thought: these threads together map a pragmatic ethics frontier—useful tooling with clear boundaries, not blind reliance, as ML research evolves.
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[D] How much should researchers (especially in ML domain) rely on LLMs for their work?
Debates on using LLMs for summarization, coding, literature search, planning, authorship, and novelty in ML research practices and ethics today.
View source[D] I’m looking for papers, preprints, datasets, or reports where an LLM is trained to only know what humans knew before a major scientific breakthrough, and is then asked to propose a new theoretical frameworkwithout using post-breakthrough knowledge and without requiring experimental validation.
Requests papers on training LLMs with historical knowledge limits to propose new theory without postbreakthrough data and evaluations emphasizing coherence
View source[R] Thesis direction: mechanistic interpretability vs semantic probing of LLM reasoning?
Undergrad weighing low-level mechanistic interpretability against high-level semantic probing to study LLM reasoning, feasibility, skills, and career effects.
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