Electronic design engineers are challenged to find relevant information
efficiently for a myriad of tasks within design construction, verification and
technology development. Large language models (LLM) have the potential to help
improve productivity by serving as conversational agents that effectively
function as subject-matter experts. In this paper we demonstrate Ask-EDA, a
chat agent designed to serve as a 24x7 expert available to provide guidance to
design engineers. Ask-EDA leverages LLM, hybrid retrieval augmented generation
(RAG) and abbreviation de-hallucination (ADH) techniques to deliver more
relevant and accurate responses. We curated three evaluation datasets, namely
q2a-100, cmds-100 and abbr-100. Each dataset is tailored to assess a distinct
aspect: general design question answering, design command handling and
abbreviation resolution. We demonstrated that hybrid RAG offers over a 40%
improvement in Recall on the q2a-100 dataset and over a 60% improvement on the
cmds-100 dataset compared to not using RAG, while ADH yields over a 70%
enhancement in Recall on the abbr-100 dataset. The evaluation results show that
Ask-EDA can effectively respond to design-related inquiries.

Ask-EDA 是一个利用大型语言模型、混合检索增强生成和去除幻象技术的机器人，它能有效回答与电子设计相关的问题。

Ask-EDA：基于 LLM、混合 RAG 和缩写去幻相的设计助手

Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and  Abbreviation De-hallucination

Federated Recommendation (FR) emerges as a novel paradigm that enables
privacy-preserving recommendations. However, traditional FR systems usually
represent users/items with discrete identities (IDs), suffering from
performance degradation due to the data sparsity and heterogeneity in FR. On
the other hand, Large Language Models (LLMs) as recommenders have proven
effective across various recommendation scenarios. Yet, LLM-based recommenders
encounter challenges such as low inference efficiency and potential
hallucination, compromising their performance in real-world scenarios. To this
end, we propose GPT-FedRec, a federated recommendation framework leveraging
ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism.
GPT-FedRec is a two-stage solution. The first stage is a hybrid retrieval
process, mining ID-based user patterns and text-based item features. Next, the
retrieved results are converted into text prompts and fed into GPT for
re-ranking. Our proposed hybrid retrieval mechanism and LLM-based re-rank aims
to extract generalized features from data and exploit pretrained knowledge
within LLM, overcoming data sparsity and heterogeneity in FR. In addition, the
RAG approach also prevents LLM hallucination, improving the recommendation
performance for real-world users. Experimental results on diverse benchmark
datasets demonstrate the superior performance of GPT-FedRec against
state-of-the-art baseline methods.

联邦推荐 （FR）是一种新的范式，它可以实现隐私保护推荐。然而，传统的 FR 系统通常使用离散标识符（IDs）来表示用户 / 物品，由于 FR 中的数据稀疏性和异质性而导致性能下降。相比之下，基于大型语言模型（LLMs）的推荐器已经在各种推荐场景中证明了其有效性。我们提出了 GPT-FedRec，这是一个利用 ChatGPT 和一种新的混合检索增强生成（RAG）机制的联邦推荐框架。GPT-FedRec 是一个两阶段的解决方案。第一阶段是混合检索过程，挖掘基于 ID 的用户模式和基于文本的物品特征。然后，将检索到的结果转换为文本提示，并输入 GPT 进行重新排序。我们提出的混合检索机制和基于 LLM 的重新排序旨在从数据中提取概括特征，并利用 LLM 内部的预训练知识，克服 FR 中的数据稀疏性和异质性。此外，RAG 方法还可以防止 LLM 的产生虚假数据，提高了针对真实用户的推荐性能。对多种基准数据集的实验结果表明，GPT-FedRec 相对于最先进的基线方法具有优越的性能。