Several tools have recently been proposed for assisting researchers during
various stages of the research life-cycle. However, these primarily concentrate
on tasks such as retrieving and recommending relevant literature, reviewing and
critiquing the draft, and writing of research manuscripts. Our investigation
reveals a significant gap in availability of tools specifically designed to
assist researchers during the challenging ideation phase of the research
life-cycle. To aid with research ideation, we propose `Acceleron', a research
accelerator for different phases of the research life cycle, and which is
specially designed to aid the ideation process. Acceleron guides researchers
through the formulation of a comprehensive research proposal, encompassing a
novel research problem. The proposals motivation is validated for novelty by
identifying gaps in the existing literature and suggesting a plausible list of
techniques to solve the proposed problem. We leverage the reasoning and
domain-specific skills of Large Language Models (LLMs) to create an agent-based
architecture incorporating colleague and mentor personas for LLMs. The LLM
agents emulate the ideation process undertaken by researchers, engaging
researchers in an interactive fashion to aid in the development of the research
proposal. Notably, our tool addresses challenges inherent in LLMs, such as
hallucinations, implements a two-stage aspect-based retrieval to manage
precision-recall trade-offs, and tackles issues of unanswerability. As
evaluation, we illustrate the execution of our motivation validation and method
synthesis workflows on proposals from the ML and NLP domain, given by 3
distinct researchers. Our observations and evaluations provided by the
researchers illustrate the efficacy of the tool in terms of assisting
researchers with appropriate inputs at distinct stages and thus leading to
improved time efficiency.

提出了一种名为 “Acceleron” 的研究加速器，旨在辅助研究生命周期的各个阶段，尤其是创意阶段。基于大型语言模型的代理人架构结合同事和导师身份，通过模拟研究员进行创作的思路，与研究者进行交互式对话，协助发展研究提案。经过评估，工具在不同阶段为研究者提供适当的输入，提高了时间效率。

Acceleron: 加速研究构想的工具

Acceleron: A Tool to Accelerate Research Ideation

Certain statistical models are capable of interpreting input strings as
instructions, or prompts, and carry out tasks based on them. Many approaches to
prompting and pre-training these models involve the automated generation of
these prompts. We call these approaches meta-prompting, or prompting to obtain
prompts. We propose a theoretical framework based on category theory to
generalize and describe them. This framework is flexible enough to account for
LLM stochasticity; and allows us to obtain formal results around task
agnosticity and equivalence of various meta-prompting approaches. We experiment
with meta-prompting in two active areas of model research: creativity and
ideation. We find that user preference favors (p < 0.01) the prompts generated
under meta-prompting, as well as their corresponding outputs, over a series of
hardcoded baseline prompts that include the original task prompt. Using our
framework, we argue that meta-prompting is more effective than basic prompting
at generating desirable outputs.

使用类别论的理论框架来描述和推广统计模型中的元提示方法，研究发现元提示方法在创意和构思领域的应用比基本提示方法更有效。

关于元提示

On Meta-Prompting

The recent advancement in Natural Language Processing (NLP) capability has
led to the development of language models (e.g., ChatGPT) that is capable of
generating human-like language. In this study, we explore how language models
can be utilized to help the ideation aspect of creative writing. Our empirical
findings show that language models play different roles in helping student
writers to be more creative, such as the role of a collaborator, a provocateur,
etc

本研究探讨了语言模型在创意写作中协助思维方面的应用，经实证发现语言模型的不同角色（如协作者、挑衅者等）为学生作家创造更多创意提供了帮助。