An LLM simulation framework generates multilingual tip-of-the-tongue queries, validated by rank correlation with real queries, producing the first large-scale ToT benchmarks for four languages.
In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval(Canberra ACT, Australia)(CHIIR ’21)
6 Pith papers cite this work. Polarity classification is still indexing.
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OBLIQ-Bench demonstrates that modern retrievers fail to surface most documents relevant to latent and implicit queries, while reasoning LLMs can reliably recognize relevance when documents are provided.
Intent Lenses infer capture-time user intent from photos via LLMs to create dynamic, reusable interactive objects that generate and organize structured visual notes for later sensemaking.
Survey indicates the intention-behavior gap in responsible purchasing often stems from information search difficulties and indifference rather than intention alone.
Information retrieval can empower socially responsible consumerism by reducing information asymmetries, supporting complex ethical searches, and calibrating consumer knowledge during product decisions.
A summary of the CHIIR 2026 workshop on Generative AI and Academic Search, covering themes of foundations, applications, and search-as-learning.
citing papers explorer
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Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
An LLM simulation framework generates multilingual tip-of-the-tongue queries, validated by rank correlation with real queries, producing the first large-scale ToT benchmarks for four languages.
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OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries
OBLIQ-Bench demonstrates that modern retrievers fail to surface most documents relevant to latent and implicit queries, while reasoning LLMs can reliably recognize relevance when documents are provided.
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Intent Lenses: Inferring Capture-Time Intent to Transform Opportunistic Photo Captures into Structured Visual Notes
Intent Lenses infer capture-time user intent from photos via LLMs to create dynamic, reusable interactive objects that generate and organize structured visual notes for later sensemaking.
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Seeking Socially Responsible Consumers: Exploring the Intention-"Search"-Behaviour Gap
Survey indicates the intention-behavior gap in responsible purchasing often stems from information search difficulties and indifference rather than intention alone.
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From Query to Conscience: The Importance of Information Retrieval in Empowering Socially Responsible Consumerism
Information retrieval can empower socially responsible consumerism by reducing information asymmetries, supporting complex ethical searches, and calibrating consumer knowledge during product decisions.
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Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)
A summary of the CHIIR 2026 workshop on Generative AI and Academic Search, covering themes of foundations, applications, and search-as-learning.