Researchers presented the new task of “backtracing” to locate the content section that likely prompted a user’s query, aiming to improve content quality and relevance. They created a benchmark for backtracing in various contexts, evaluated retrieval systems, and emphasized the need for algorithms to accurately capture causal linkages between queries and information.
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Backtracing and Retrieving the Cause of the Query: A Practical AI Solution
In a recent study, researchers have addressed the drawbacks of current online content portals that hinder users from pinpointing the exact parts of material that prompted their questions. This has given rise to the development of the new task of backtracing, to obtain the text segment that is most likely the source of a user’s query.
Practical Domains
Three practical domains have been identified to formalize the backtracing job, aiming to enhance communication and content distribution:
- ‘Lecture’ domain: Figuring out the root of students’ uncertainty
- ‘News article’ area: Understanding the cause of reader curiosity
- ‘Conversation’ domain: Determining the reason behind a user’s reaction
A zero-shot evaluation has demonstrated the potential for progress in backtracing, calling for the creation of fresh retrieval strategies to capture causally important context and enhance content generation.
The primary contributions of the research include the presentation of the backtracing task, creation of a benchmark in various contexts, and assessment of well-known retrieval systems for their capacity to deduce the causal relationship between user searches and content segments.
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