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Found 8 papers, 1 papers with code
The Surprising Effectiveness of Rankers Trained on Expanded Queries
In our extensive experiments on the DL-Hard dataset, we find that a principled query performance based scoring method using base and specialized ranker offers a significant improvement of up to 25% on the passage ranking task and up to 48. 4% on the document ranking task when compared to the baseline performance of using original queries, even outperforming SOTA model.
NUMTEMP: A real-world benchmark to verify claims with statistical and temporal expressions
This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, not addressed by existing works that mainly focus on synthetic claims.
Data Augmentation for Sample Efficient and Robust Document Ranking
We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model.
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Dual-encoder-based dense retrieval models have become the standard in IR.
Context Aware Query Rewriting for Text Rankers using LLM
We find that there are two inherent limitations of using LLMs as query re-writers -- concept drift when using only queries as prompts and large inference costs during query processing.
Query Understanding in the Age of Large Language Models
Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM).
Supervised Contrastive Learning Approach for Contextual Ranking
This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem.
Efficient Neural Ranking using Forward Indexes
In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search.
