Search Results for author: Ramraj Chandradevan

Found 5 papers, 3 papers with code

Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback

no code implementations27 May 2024 Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein

Query Reformulation (QR) is a set of techniques used to transform a user's original search query to a text that better aligns with the user's intent and improves their search experience.

Retrieval

DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation

1 code implementation3 Apr 2024 Ramraj Chandradevan, Kaustubh D. Dhole, Eugene Agichtein

State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot.

Unsupervised Domain Adaptation

QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration

1 code implementation23 Mar 2024 Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan, Eugene Agichtein

To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer -- an interactive query generation, reformulation, and retrieval interface with support for HuggingFace generation models and PyTerrier's retrieval pipelines and datasets, and extensive logging of human feedback.

Retrieval

An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback

2 code implementations19 Nov 2023 Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein

While search is the predominant method of accessing information, formulating effective queries remains a challenging task, especially for situations where the users are not familiar with a domain, or searching for documents in other languages, or looking for complex information such as events, which are not easily expressible as queries.

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