Today’s online platforms, e.g. in e-commerce, often offer users numerous competing options in single product categories, e.g. televisions or watches, making it difficult for the users to identify the best option to suit their preferences. To ease this process, many platforms provide users with simple scores resulting from the aggregation of other users’ reviews. However, these scoring systems may oversimplify the underlying information and lack explanatory context. Our main contribution in this demonstration paper is a novel online system for aggregating customer reviews and explaining the aggregation to users.The system operates through a multi-stage pipeline: it first applies novel automatic ontology extraction methods using BERT or Large Language Models to identify key aspects from customer reviews, then constructs support and attack relations between these aspects using Argumentative Dialogical Agents (ADAs), an existing methodology for generating argumentative analyses of aspects. Finally, it generates ontology-driven, explainable aggregations of the reviews. We evaluate the performance of our system (which we call ADA-X) on the Amazon and Disneyland review datasets, focusing the ontology quality using the LLM-as-a-judge method and aggregation performance against the original Amazon and Disneyland ratings. The demonstration is available at https://ada-x.co.uk/.
Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous nodes, reducing their effectiveness in knowledge retrieval. To address this, we present Taxoria, a novel taxonomy enrichment pipeline that leverages Large Language Models (LLMs) to enhance a given taxonomy. Unlike approaches that extract internal LLM taxonomies, Taxoria uses an existing taxonomy as a seed and prompts an LLM to propose candidate nodes for enrichment. These candidates are then validated to mitigate hallucinations and ensure semantic relevance before integration. The final output includes an enriched taxonomy with provenance tracking and visualization of the final merged taxonomy for analysis.
It is known that a simple numeric planning problem (SNP) with one numeric variable is decidable but undecidable with three (Helmert 2002). A more recent result (Gnad et. al 2023) showed undecidability for two numeric and one propositional variable. In this paper, we show the decidability of SNP with exactly two numeric variables. For this, we first partition the state space into a finite number of regions and demonstrate the decidability of SNP when restricted to any of these regions. Afterwards, we develop a correct search algorithm that abstracts from these regions by tracking an infinite number of states following an arithmetic progression pattern. Finally, we prove termination of the search and draw conclusions about the reasons for undecidability for general SNP.
This paper presents FactNET, a framework for human-centered granular fact-checking. Each claim to check is decomposed into sub claims that are fact-checked independently by using an LLM’s internal knowledge. The user interacts with the framework through an innovative interface, displaying the complex claim in the form of a graph, allowing the input of human knowledge on the topic, and evaluating the trust given in the generated evidence. A video demonstrating the system is available at TO_BE_PUBLISHED (in submission documents for review phase).
People have long asked complex existential and ethical questions, e.g., 'What is a meaningful life?' or 'How can I address inequality?' Theological texts offer deep insights on these critical questions, but their complexity often limits their access to a larger audience. We present a system that uses retrieval-augmented generation (RAG) with large language models (LLMs) to make this wisdom more accessible. By retrieving content from theological sources and synthesizing it into understandable answers, the system addresses a wide range of ethical questions. Technically, we propose ensemble RAG methods with iterative query simplification to align better with the ancient vocabulary. Our application highlights the potential of AI to support ethical reflection. A video is available here.
