Large language models (LLMs) have offered new opportunities for emotional
support, and recent work has shown that they can produce empathic responses to
people in distress. However, long-term mental well-being requires emotional
self-regulation, where a one-time empathic response falls short. This work
takes a first step by engaging with cognitive reappraisals, a strategy from
psychology practitioners that uses language to targetedly change negative
appraisals that an individual makes of the situation; such appraisals is known
to sit at the root of human emotional experience. We hypothesize that
psychologically grounded principles could enable such advanced psychology
capabilities in LLMs, and design RESORT which consists of a series of
reappraisal constitutions across multiple dimensions that can be used as LLM
instructions. We conduct a first-of-its-kind expert evaluation (by clinical
psychologists with M.S. or Ph.D. degrees) of an LLM's zero-shot ability to
generate cognitive reappraisal responses to medium-length social media messages
asking for support. This fine-grained evaluation showed that even LLMs at the
7B scale guided by RESORT are capable of generating empathic responses that can
help users reappraise their situations.

通过使用心理学实践中的认知重评方式，本研究设计出一种具有高级心理能力的大型语言模型，经过专家评估证实，即使是由 RESORT 引导的规模为 7B 的大型语言模型也能够生成能够帮助用户认知重评自身情况的共情回应。

大型语言模型可以提供认知重评，如果有指导

Large Language Models are Capable of Offering Cognitive Reappraisal, if  Guided

Large Language Models (LLMs), with their flexible generation abilities, can
be powerful data sources in domains with few or no available corpora. However,
problems like hallucinations and biases limit such applications. In this case
study, we pick nutrition counselling, a domain lacking any public resource, and
show that high-quality datasets can be gathered by combining LLMs,
crowd-workers and nutrition experts. We first crowd-source and cluster a novel
dataset of diet-related issues, then work with experts to prompt ChatGPT into
producing related supportive text. Finally, we let the experts evaluate the
safety of the generated text. We release HAI-coaching, the first
expert-annotated nutrition counselling dataset containing ~2.4K dietary
struggles from crowd workers, and ~97K related supportive texts generated by
ChatGPT. Extensive analysis shows that ChatGPT while producing highly fluent
and human-like text, also manifests harmful behaviours, especially in sensitive
topics like mental health, making it unsuitable for unsupervised use.

使用大型语言模型，结合众包工人和营养专家，在缺乏公共资源的营养咨询领域，成功地聚集了一系列高质量的数据集，并通过专家评估得出，ChatGPT 生成的文本在流畅度和人类化方面表现出色，但在敏感话题（如心理健康）中存在不利行为，不适合无监督使用。

借助人工智能协作：为营养咨询寻找高质量数据集

Ask the experts: sourcing high-quality datasets for nutritional  counselling through Human-AI collaboration

Literary translation is a culturally significant task, but it is bottlenecked
by the small number of qualified literary translators relative to the many
untranslated works published around the world. Machine translation (MT) holds
potential to complement the work of human translators by improving both
training procedures and their overall efficiency. Literary translation is less
constrained than more traditional MT settings since translators must balance
meaning equivalence, readability, and critical interpretability in the target
language. This property, along with the complex discourse-level context present
in literary texts, also makes literary MT more challenging to computationally
model and evaluate. To explore this task, we collect a dataset (Par3) of
non-English language novels in the public domain, each aligned at the paragraph
level to both human and automatic English translations. Using Par3, we discover
that expert literary translators prefer reference human translations over
machine-translated paragraphs at a rate of 84%, while state-of-the-art
automatic MT metrics do not correlate with those preferences. The experts note
that MT outputs contain not only mistranslations, but also discourse-disrupting
errors and stylistic inconsistencies. To address these problems, we train a
post-editing model whose output is preferred over normal MT output at a rate of
69% by experts. We publicly release Par3 at
this https URL to spur future research into literary
MT.

本研究提出了一种文学机器翻译方法，通过 Par3 数据集，证明专业翻译员更喜欢校对后的机器翻译输出，而不是普通的机器翻译输出。