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Generate to Accelerate: Improved Reranking via LLM-Generated Pivot Documents
Nilanjan Sinhababu, Soumedhik Bharati, Debasis Ganguly +1
Common approaches to reduce the computational overhead of reranking models include identifying a candidate set of documents for reranking or constructing comparison graphs to minim…
Modeling Ranking Properties with In-Context Learning
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly +1
While standard IR models are primarily designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectiv…
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly +2
A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning.…