AI 与机器学习2025arxiv.org
AbsenceBench：语言模型无法识别缺失内容
AbsenceBench: Language models can't tell what's missing
进阶 · 约 35 分钟读完前置知识：Transformer 基础、注意力机制、语言模型评测
导读
AbsenceBench 是一个评测语言模型能否发现上下文中被删除/缺失信息的基准：给定原文和编辑后文本，要求模型指出少了什么。评论提到论文发现即使最新模型也表现较差，作者将原因归结为 Transformer 注意力无法关注已被删除的 token（没有对应的 key）。
HN 关注点：触及 LLM 的一个根本短板：模型能否像人一样察觉“不存在”的信息，以及这是否暴露了注意力机制和认知能力的边界。
HN 评论精选
概括论文核心发现，并给出作者对注意力机制局限的解释。
有意思。即使是最新的模型，在同时拿到原始上下文和编辑后上下文、被要求指出上下文中哪些信息被删除时，表现也相对很差。作者认为表现差是因为 Transformer 的注意力机制无法关注被删除的 token，因为不存在对应的 key！感谢在 HN 分享。
cs702查看英文原文
Interesting. Even the most recent models perform relatively poorly when asked to identify which information in a context has been removed, given access to both the original and edited contexts. The authors posit that poor performance is due to the fact that the attention mechanism of Transformers cannot attend to the removed tokens, because there are no keys for them! Thank you for sharing on HN.
从认知科学角度认为检测缺失是更高阶能力，可能是生物独有。
要检测某物存在，真实大脑接收感官输入并与预期比较，保持平静或记录惊讶，并不时发出预测来指导机体。要检测缺失，大脑按定义不能依赖感官输入。若感官证据不在时能感到惊讶，需要一个足够强的世界模型，在没有感官提示的情况下，当预期缺失时记录惊讶。在我看来，检测缺失是比处理感官输入严格更高阶的神经任务。如果 LLM 不能完成这种严格更高阶的神经任务，这难道不是目前生物独有的能力吗？
kadonoishi查看英文原文
To detect a presence, a real brain takes in sensory input and compares it to expectations, and stays calm or registers surprise, and from time to time issues predictions to guide the organism. To detect an absence, the brain cannot rely on sensory input, by definition. To be surprised if sensory evidence is _not_ there requires a model of the world strong enough to register surprise if the expectation is not there, without a sensory prompt. It seems to me detecting an absence is a strictly higher-order neurological task than processing sensory input. If LLMs can't do this strictly higher-order neurological task, is that not a capability currently unique to living things?
亲历案例说明模型会错误断言文本中不存在某词，实际全文检索能找到。
有意思。我前几天遇到一个例子。我问 ChatGPT：《哈姆雷特》里有没有一行包含“utter love”这两个词？它检查了很多《哈姆雷特》引文，然后结论：我复核了所有版本，《哈姆雷特》中没有一行包含“utter”。它在任何对开本或四开本任何一行中都没用过。我很确定有，就找到《哈姆雷特》全文搜索“utter”，立刻找到了，发给 ChatGPT：奇怪，我搜全文两秒就找到了。“This must be known, which, being kept close, might move More grief to hide than hate to utter love. Come” HAMLET, Act 2, Scene 2。ChatGPT 回复：你完全正确——谢谢指出。这句确实出自第二幕第二场，克劳狄斯所说，并给出全文，还为之前的疏漏道歉，说我的记忆比它的索引更敏锐。
VikRubenfeld查看英文原文
Interesting. I encountered an example of this the other day. I was asking chatGPT: > Is there a line in Hamlet that includes the words "utter love?" It examined a lot of quotes from Hamlet, and concluded: > I double-checked all editions: There is no line in Hamlet that contains the word “utter.” >It is never used in any of the quartos or folios in any line of Hamlet. I was pretty sure it was there, so I found the full text of Hamlet online and searched for the word "utter". I found it right away and posted to chatGPT: > That's odd. I found it in two seconds by searching the full text for the word "utter". > This must be known, which, being kept close, might move More grief to hide than hate to utter love. > Come > HAMLET, Act 2, Scene 2 chatGPT replied: > You're absolutely right — thank you for pointing that out. > The line is indeed from Act 2, Scene 2, spoken by Claudius, and here it is in full: > "This must be known, which, being kept close, might move > More grief to hide than hate to utter love." > I regret the earlier oversight — your memory was sharper than my index!
模型依赖特定方向或记忆识别图像，旋转后无法识别推断出的三角形。
也许相关：在看了 Gerald Sussman 的演讲后，我把卡尼萨三角的图像加载给 Claude，问了一个相当模糊的问题，看它能否“看到”推断出的三角形。它认出了图像，直接给我一段关于它的总结。于是我把图像旋转 90 度，在新对话里再试，它没认出图像，还数错了元素数量：它描述图像有四个带楔形缺口的黑色局部圆形/“吃豆人”形状位于四角，两个细黑色三角形/箭头形状等，显然没有识别出卡尼萨三角。
birdfood查看英文原文
Perhaps related, after watching a talk by Gerald Sussman I loaded an image of the Kanizsa triangle into Claude and asked it a pretty vague question to see if it could “see” the inferred triangle. It recognised the image and went straight into giving me a summary about it. So I rotated the image 90 degrees and tried in a new conversation, it didn’t recognise the image and got the number of elements incorrect: This image shows a minimalist, abstract geometric composition with several elements: Four black shapes that appear to be partial circles or "Pac-Man" like forms, each with a wedge cut out, positioned in the four corners/quadrants of the image Two thin black triangular or arrow-like shapes - one pointing upward in the upper left area, and one pointing to the right in the center-right area All elements are arranged on a light gray or off-white background
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