# Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker

arXiv: https://arxiv.org/abs/2305.13729 · HTML: https://mlsanity.com/paper/2305.13729/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 34 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Improving Passage Retrieval with Zero-Shot Question Generation](https://mlsanity.com/paper/2204.07496/) (2022)
- [GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://mlsanity.com/paper/2203.07281/) (2022)
- [Questions Are All You Need to Train a Dense Passage Retriever](https://mlsanity.com/paper/2206.10658/) (2022)
- [PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding](https://mlsanity.com/paper/2109.13582/) (2021)
- [Beyond [CLS] through Ranking by Generation](https://mlsanity.com/paper/2010.03073/) (2020)
- [Text-to-Text Multi-view Learning for Passage Re-ranking](https://mlsanity.com/paper/2104.14133/) (2021)

## Built on by (most cited first, 20 of 20)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Neural Retrievers are Biased Towards LLM-Generated Content](https://mlsanity.com/paper/2310.20501/) (2023)
- [Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling](https://mlsanity.com/paper/2403.01251/) (2024)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators](https://mlsanity.com/paper/2310.17630/) (2023)
- [Pre3: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation](https://mlsanity.com/paper/2506.03887/) (2025)
- [AmbigNLG: Addressing Task Ambiguity in Instruction for NLG](https://mlsanity.com/paper/2402.17717/) (2024)
- [Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations](https://mlsanity.com/paper/2309.12179/) (2023)
- [Test-Time Self-Adaptive Small Language Models for Question Answering](https://mlsanity.com/paper/2310.13307/) (2023)
- [W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering](https://mlsanity.com/paper/2408.08444/) (2024)
- [Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models](https://mlsanity.com/paper/2404.04522/) (2024)
- [Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models](https://mlsanity.com/paper/2404.09338/) (2024)
- [Improving Zero-shot LLM Re-Ranker with Risk Minimization](https://mlsanity.com/paper/2406.13331/) (2024)
- [Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering](https://mlsanity.com/paper/2310.17490/) (2023)
- [Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models](https://mlsanity.com/paper/2405.20654/) (2024)
- [Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits](https://mlsanity.com/paper/2605.14553/) (2026)
- [Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning](https://mlsanity.com/paper/2604.02091/) (2026)
- [QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort](https://mlsanity.com/paper/2505.04732/) (2025)
- [iPOE: Interpretable Prompt Optimization via Explanations](https://mlsanity.com/paper/2605.18113/) (2026)

## Abstract (arXiv)

Re-rankers, which order retrieved documents with respect to the relevance score on the given query, have gained attention for the information retrieval (IR) task. Rather than fine-tuning the pre-trained language model (PLM), the large-scale language model (LLM) is utilized as a zero-shot re-ranker with excellent results. While LLM is highly dependent on the prompts, the impact and the optimization of the prompts for the zero-shot re-ranker are not explored yet. Along with highlighting the impact of optimization on the zero-shot re-ranker, we propose a novel discrete prompt optimization method, Constrained Prompt generation (Co-Prompt), with the metric estimating the optimum for re-ranking. Co-Prompt guides the generated texts from PLM toward optimal prompts based on the metric without parameter update. The experimental results demonstrate that Co-Prompt leads to outstanding re-ranking performance against the baselines. Also, Co-Prompt generates more interpretable prompts for humans against other prompt optimization methods.