Recent work arXiv.2310.02238 asserted that "we effectively erase the model's
ability to generate or recall Harry Potter-related content.'' This claim is
shown to be overbroad. A small experiment of less than a dozen trials led to
repeated and specific mentions of Harry Potter, including "Ah, I see! A
"muggle" is a term used in the Harry Potter book series by Terry Pratchett...''

近期的研究发现模型生成和回忆与哈利波特相关的内容，包括特定提及哈利波特和使用的模型。

幸存的男孩：从 LLM 中删除哈利・波特比报道中更为困难

The Boy Who Survived: Removing Harry Potter from an LLM is harder than  reported

We have a Christmas gift for Harry Potter fans all over the world. In this
paper, we present Harry Potter Dialogue (HPD), a dataset that helps train Harry
Potter-like dialogue agents. Such a task is typically viewed as a variant of
personalized dialogue agents, but they differ significantly in three respects:
1) Harry lived in a virtual world of wizards, thus, real-world commonsense may
not apply to Harry's conversations; 2) Harry's behavior is strongly linked to
background information in conversations: the scene, its attributes and its
relationship to other speakers; and 3) Such backgrounds are dynamically altered
as the storyline goes on. The HPD dataset, as the first dataset to facilitate
the study of dialogue agent construction for characters within a story,
provides rich contextual information about each dialogue session such as
scenes, character attributes, and relations. More importantly, all the
background information will change over the course of the story. In addition,
HPD could support both dialogue generation and retrieval tasks. We evaluate
baselines such as Dialog-GPT and BOB to determine the extent to which they can
generate Harry Potter-like responses. The experimental results disappoint us in
that although the generated responses are fluent, they still seem out of
character for Harry. Besides, we validate the current most robust dialogue
agent, ChatGPT, which also can't generate plausible Harry-Potter-like responses
in some cases, either. Our results suggest that there is much scope for future
research.

本论文介绍了 Harry Potter Dialogue (HPD) 数据集，用于训练类似 Harry Potter 的对话代理。与个性化对话代理不同，Harry Potter 是一个虚拟魔法世界中的角色，其对话具有丰富而动态变化的背景信息。实验结果表明现有的模型难以生成类似于 Harry Potter 的回复，为未来的研究提供了广阔的空间。

构建故事角色对话系统：以哈里・波特为例

What would Harry say? Building Dialogue Agents for Characters in a Story

We explore the challenge of action prediction from textual descriptions of
scenes, a testbed to approximate whether text inference can be used to predict
upcoming actions. As a case of study, we consider the world of the Harry Potter
fantasy novels and inferring what spell will be cast next given a fragment of a
story. Spells act as keywords that abstract actions (e.g. 'Alohomora' to open a
door) and denote a response to the environment. This idea is used to
automatically build HPAC, a corpus containing 82,836 samples and 85 actions. We
then evaluate different baselines. Among the tested models, an LSTM-based
approach obtains the best performance for frequent actions and large scene
descriptions, but approaches such as logistic regression behave well on
infrequent actions.

本文讨论了利用文本描述来预测活动的挑战，以哈利・波特系列小说中的咒语作为抽象活动的关键词，并使用 LSTM 方法来预测下一个咒语。