Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges by selectively extracting relevant user data (i.e. selective retrieval) to construct a prompt for downstream tasks. However, retrieval-based methods are limited by potential information loss, lack of more profound user understanding, and cold-start challenges. To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs. The summaries can be generated and stored offline, enabling real-world systems with runtime constraints like voice assistants to leverage the power of LLMs. Experiments show our method with 75% less of retrieved user data is on-par or outperforms retrieval augmentation on most tasks in the LaMP personalization benchmark. We demonstrate that offline summarization via LLMs and runtime retrieval enables better performance for personalization on a range of tasks under practical constraints.Comment: 4 pages, International Workshop on Personalized Generative AI (@CIKM 2023

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Spatial Planning For Placement Command Understanding

Object placement in virtual and real worlds is an important task for autonomous agents and applications. Interacting with agents using natural language commands presents an intuitive alternative to graphical and other operator control interfaces. However, understanding and interpreting language for placement actions in 3D continuous spaces is computationally hard. In this paper, we extend our previous work on resolving underspecified linguistic commands for object placement using a spatial planning approach called object placement using ordered application and simulation of constraints (OPOCS). This heuristic approach represents and utilizes practical real-world knowledge of objects an their interactions. It does this with simulation of human activities to accurately place and orient objects. We evaluated OPOCS and compared it with a naïve algorithm for understanding 10 representative spatial terms and relations in 4 different 3D office worlds. Our findings show that OPOCS significantly outperforms a naïve algorithm. 1