Large language models (LLMs) excel at a variety of natural language
processing tasks, yet they struggle to generate personalized content for
individuals, particularly in real-world scenarios like scientific writing.
Addressing this challenge, we introduce Step-Back Profiling to personalize LLMs
by distilling user history into concise profiles, including essential traits
and preferences of users. Regarding our experiments, we construct a
Personalized Scientific Writing (PSW) dataset to study multiuser
personalization. PSW requires the models to write scientific papers given
specialized author groups with diverse academic backgrounds. As for the
results, we demonstrate the effectiveness of capturing user characteristics via
Step-Back Profiling for collaborative writing. Moreover, our approach
outperforms the baselines by up to 3.6 points on the general personalization
benchmark (LaMP), including 7 personalization LLM tasks. Our extensive ablation
studies validate the contributions of different components in our method and
provide insights into our task definition. Our dataset and code are available
at https://github.com/gersteinlab/step-back-profiling.

使用 Step-Back Profiling 方法将大型语言模型个性化，以便在科学写作等实际场景中生成个性化内容。实验结果表明，我们的方法在个性化基准 (LaMP) 上相对于基准方法提高了 3.6 个点，包括 7 个个性化大型语言模型任务。

Step-Back Profiling: 提取用户历史信息以个性化科学写作

Step-Back Profiling: Distilling User History for Personalized Scientific  Writing

Large language models (LLMs) have led to a surge in collaborative writing
with model assistance. As different users incorporate suggestions from the same
model, there is a risk of decreased diversity in the produced content,
potentially limiting diverse perspectives in public discourse. In this work, we
measure the impact of co-writing on diversity via a controlled experiment,
where users write argumentative essays in three setups -- using a base LLM
(GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We
develop a set of diversity metrics and find that writing with InstructGPT (but
not the GPT3) results in a statistically significant reduction in diversity.
Specifically, it increases the similarity between the writings of different
authors and reduces the overall lexical and content diversity. We additionally
find that this effect is mainly attributable to InstructGPT contributing less
diverse text to co-written essays. In contrast, the user-contributed text
remains unaffected by model collaboration. This suggests that the recent
improvement in generation quality from adapting models to human feedback might
come at the cost of more homogeneous and less diverse content.

通过对大型语言模型的合作写作进行实验，我们测量了共同撰写对多样性的影响，发现使用 InstructGPT 会显著降低多样性，增加作者之间的文字相似性和降低词汇和内容多样性。这表明通过模型适应人类反馈来提高生成质量可能以减少多样性为代价。

语言模型书写是否减少内容多样性？

Does Writing with Language Models Reduce Content Diversity?

Textual content is often the output of a collaborative writing process: We
start with an initial draft, ask for suggestions, and repeatedly make changes.
Agnostic of this process, today's language models are trained to generate only
the final result. As a consequence, they lack several abilities crucial for
collaborative writing: They are unable to update existing texts, difficult to
control and incapable of verbally planning or explaining their actions. To
address these shortcomings, we introduce PEER, a collaborative language model
that is trained to imitate the entire writing process itself: PEER can write
drafts, add suggestions, propose edits and provide explanations for its
actions. Crucially, we train multiple instances of PEER able to infill various
parts of the writing process, enabling the use of self-training techniques for
increasing the quality, amount and diversity of training data. This unlocks
PEER's full potential by making it applicable in domains for which no edit
histories are available and improving its ability to follow instructions, to
write useful comments, and to explain its actions. We show that PEER achieves
strong performance across various domains and editing tasks.

本研究介绍了 PEER，一种协作语言模型，可以模仿整个写作过程，撰写草稿，添加建议，提出编辑意见并解释其行为。在各种领域和编辑任务中表现出强大的性能，并通过使用自我训练技术提高了训练数据的质量、数量和多样性，从而发挥了其全部潜力。