Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or errors in automatic speech recognition (ASR) and natural language understanding (NLU). Personalized query rewriting is an approach that focuses on reducing defects in queries by taking into account the user's individual behavior and preferences. It typically relies on an index of past successful user interactions with the conversational AI. However, unseen interactions within the user's history present additional challenges for personalized query rewriting. This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history. This approach builds a "User Feedback Interaction Graph" (FIG) of historical user-entity interactions and leverages multi-hop graph traversal to enrich each user's index to cover future unseen defective queries. The enriched user index is called a Collaborative User Index and contains hundreds of additional entries. To counteract precision degradation from the enlarged index, we add additional transformer layers to the L1 retrieval model and incorporate graph-based and guardrail features into the L2 ranking model. Since the user index can be pre-computed, we further investigate the utilization of a Large Language Model (LLM) to enhance the FIG for user-entity link prediction in the Video/Music domains. Specifically, this paper investigates the Dolly-V2 7B model. We found that the user index augmented by the fine-tuned Dolly-V2 generation significantly enhanced the coverage of future unseen user interactions, thereby boosting QR performance on unseen queries compared with the graph traversal only approach.

翻译：对话式AI系统（如Alexa）需理解缺陷查询以保障鲁棒对话理解并减少用户摩擦。此类缺陷查询常源于用户歧义、错误或自动语音识别（ASR）与自然语言理解（NLU）中的失误。个性化查询重写是一种通过考虑个体用户行为与偏好来减少查询缺陷的方法，通常依赖对话式AI中历史成功用户交互的索引。然而，用户历史中未出现的交互为个性化查询重写带来了额外挑战。本文提出“协同查询重写”方法，专攻对用户历史中未曾观测到的新交互进行重写任务。该方法构建历史用户-实体交互的“用户反馈交互图（FIG）”，并利用多跳图遍历扩充每个用户的索引以覆盖未来未见的缺陷查询。扩充后的索引称为协同用户索引，包含数百条额外条目。为抵消索引扩大带来的精度下降，我们在L1检索模型中增加额外Transformer层，并在L2排序模型中融入图特征与护栏特征。由于用户索引可预计算，我们进一步探索利用大语言模型（LLM）增强视频/音乐领域用户-实体链接预测的FIG。具体而言，本文研究了Dolly-V2 7B模型。实验表明，经微调Dolly-V2生成的用户索引显著提升了对未来未见用户交互的覆盖率，相较于仅采用图遍历的方法，在未见查询上大幅提升了查询重写（QR）性能。