{
  "video_id": "reddit_1uv1xb3",
  "channel_slug": "MachineLearning",
  "channel_handle": "r/MachineLearning",
  "title": "Prompt-engineering paper accepted to ICML [R]",
  "url": "https://www.reddit.com/r/MachineLearning/comments/1uv1xb3/promptengineering_paper_accepted_to_icml_r/",
  "external_url": null,
  "upload_date": "20260713",
  "published_at": "2026-07-13T05:00:07+00:00",
  "transcript": "\"[Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity](https://arxiv.org/abs/2510.01171)\"\n\nThis paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: \"changing the prompt this way led to more diverse sampling\". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this.\n\nEven if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues.\n\nHow do you think? Am I being too rigid?\n\n\n\n--- Top Comments ---\n\n\n[159 upvotes] Wait, you mean to tell me that publishing in machine learning has really, really taken an over all turn for the worse and is arguably worse than psychology was two decades ago?\n\nSurely no one could have seen this coming. \n\n[50 upvotes] >Even if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues.\n\nI'm not surprised OP feels this way. By OP's logic, the Chain of Thought paper is also \"just a prompt engineering\" paper since it just asks the model to \"think step by step.\"\n\nThe broader problem behind such nonsensical gatekeeping is that unfortunately, there is a large section of researchers like OP that have made \"prompt engineering\" into a derogatory dogwhistle for papers that don't follow their arbitrary pre-2022 standard of what an ML research paper \"should look like.\" Most of them have still not emotionally processed the trauma of ChatGPT and feel stuck. The fact is that even simple prompt engineering experiments can offer us a deep window into the underlying mechanisms of these large language models.\n\nOP, I don't mean to be harsh, but what counts as \"proper science\" is not defined by anything more than 1) observation, 2) hypothesizing, 3) experimentation, 4) analysis, and 5) offering a valuable contribution to an interested comm\n\n[43 upvotes] I mean it's prompt engineering within a very specific under explored application wrt preference alignment with theoretical formalization of the problem that they want to study. Prompt engineering isn't bad because it's bad it's bad because it's too simple for most problems people wanted to apply it for (we got 5% improvement by engineering the system prompt etc). This is a complex problem where it's surprising that prompt engineering helps when deployed at automated scale so I don't see a problem with it? If they proposed a more complex solution I would feel they were overengineering \n\n[36 upvotes] It’s all in the framing. Get a simple idea (this is not necessarily bad btw) and pair it with decent framing of your problem + rigorous experiments can get you a long way including getting accepted to top venues. ",
  "transcript_chars": 2971,
  "ingested_at": "2026-07-13T13:30:06.932581+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 141,
    "upvote_ratio": 0.9,
    "num_comments": 33,
    "author": "Mean_Revolution1490",
    "is_self": true
  }
}