Papers Passage Reranking

Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?

With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can be integrated into p…

Rank-K: Test-Time Reasoning for Listwise Reranking

Retrieve-and-rerank is a popular retrieval pipeline because of its ability to make slow but effective rerankers efficient enough at query time by reducing the number of comparisons. Recent works in neural rerankers take …

How Relevance Emerges: Interpreting LoRA Fine-Tuning in Reranking LLMs

We conduct a behavioral exploration of LoRA fine-tuned LLMs for Passage Reranking to understand how relevance signals are learned and deployed by Large Language Models. By fine-tuning Mistral-7B, LLaMA3.1-8B, and Pythia-…

Understanding Ranking LLMs: A Mechanistic Analysis for Information Retrieval

Transformer networks, particularly those achieving performance comparable to GPT models, are well known for their robust feature extraction abilities. However, the nature of these extracted features and their alignment w…

Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models

Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new state-of-the-art in this task. However, the …

Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models

Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to LLMs for reranking the retrieved passag…

TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking

In this paper, we present TWOLAR: a two-stage pipeline for passage reranking based on the distillation of knowledge from Large Language Models (LLM). TWOLAR introduces a new scoring strategy and a distillation process co…

InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models

This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training with query-document pairs or retrieval-spe…

EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models

Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strat…

Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models

Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistency, a form of self-consistency over ranki…

Reranking Passages with Coarse-to-Fine Neural Retriever Enhanced by List-Context Information

Passage reranking is a critical task in various applications, particularly when dealing with large volumes of documents. Existing neural architectures have limitations in retrieving the most relevant passage for a given …

Hybrid and Collaborative Passage Reranking

In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between que…

Learning List-Level Domain-Invariant Representations for Ranking

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distri…

PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN

Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have incorporated some reading capability into…

How Does BERT Rerank Passages? An Attribution Analysis with Information Bottlenecks

Fine-tuned pre-trained transformers achieve the state of the art in passage reranking. Unfortunately, how they make their predictions remains vastly unexplained, especially at the end-to-end, input-to-output level. Littl…

Reader-Guided Passage Reranking for Open-Domain Question Answering

Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval

Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural models, we use a previous technique named F…

Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering

Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In th…

Making Information Seeking Easier: An Improved Pipeline for Conversational Search

This paper presents a highly effective pipeline for passage retrieval in a conversational search setting. The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR). CTS …

A Study of Latent Structured Prediction Approaches to Passage Reranking

The structured output framework provides a helpful tool for learning to rank problems. In this paper, we propose a structured output approach which regards rankings as latent variables. Our approach addresses the complex…