# List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation

arXiv: https://arxiv.org/abs/2402.02764 · HTML: https://mlsanity.com/paper/2402.02764/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 2 of 2)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GERE: Generative Evidence Retrieval for Fact Verification](https://mlsanity.com/paper/2204.05511/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Survey on Retrieval-Augmented Text Generation for Large Language Models](https://mlsanity.com/paper/2404.10981/) (2024)
- [Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation](https://mlsanity.com/paper/2402.18150/) (2024)
- [A Theory for Token-Level Harmonization in Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00944/) (2024)
- [Towards a Robust Retrieval-Based Summarization System](https://mlsanity.com/paper/2403.19889/) (2024)
- [FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters](https://mlsanity.com/paper/2602.05235/) (2026)
- [LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2602.13571/) (2026)
- [STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering](https://mlsanity.com/paper/2604.17405/) (2026)

## Abstract (arXiv)

The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generation for large language models (LLMs). List-aware retrieval aims to capture the list-level contextual features to return a better list, mainly including reranking and truncation. Reranking finely re-scores the documents in the list. Truncation dynamically determines the cut-off point of the ranked list to achieve the trade-off between overall relevance and avoiding misinformation from irrelevant documents. Previous studies treat them as two separate tasks and model them separately. However, the separation is not optimal. First, it is hard to share the contextual information of the ranking list between the two tasks. Second, the separate pipeline usually meets the error accumulation problem, where the small error from the reranking stage can largely affect the truncation stage. To solve these problems, we propose a Reranking-Truncation joint model (GenRT) that can perform the two tasks concurrently. GenRT integrates reranking and truncation via generative paradigm based on encoder-decoder architecture. We also design the novel loss functions for joint optimization to make the model learn both tasks. Sharing parameters by the joint model is conducive to making full use of the common modeling information of the two tasks. Besides, the two tasks are performed concurrently and co-optimized to solve the error accumulation problem between separate stages. Experiments on public learning-to-rank benchmarks and open-domain Q&A tasks show that our method achieves SOTA performance on both reranking and truncation tasks for web search and retrieval-augmented LLMs.