mlsanityData to 14 Oct 2026

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

arXiv 2305.13729 ↗First posted 23 May 2023cs.IR cs.AI cs.CLcited by 20

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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.

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

27 of 34 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  7. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  8. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  9. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  10. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  11. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  12. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
15 more
  1. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 799
  2. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 762
  3. 2021GPT Understands, Toocited by 716
  4. 2019Passage Re-ranking with BERTcited by 645
  5. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 413
  6. 2020GeDi: Generative Discriminator Guided Sequence Generationcited by 378
  7. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  8. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
  9. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  10. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 165
  11. 2022GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Modelscited by 127
  12. 2022Questions Are All You Need to Train a Dense Passage Retrievercited by 47
  13. 2021PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decodingcited by 33
  14. 2020Beyond [CLS] through Ranking by Generationcited by 20
  15. 2021Text-to-Text Multi-view Learning for Passage Re-rankingcited by 14

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Citing papers per year: 2023: 6; 2024: 8; 2025: 3; 2026: 3.

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  1. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  2. 2023Neural Retrievers are Biased Towards LLM-Generated Contentcited by 38
  3. 2024Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Samplingcited by 37
  4. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  5. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Rerankingcited by 28
  6. 2023InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operatorscited by 17
  7. 2025Pre3: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generationcited by 11
  8. 2024AmbigNLG: Addressing Task Ambiguity in Instruction for NLGcited by 10
  9. 2023Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representationscited by 9
  10. 2023Test-Time Self-Adaptive Small Language Models for Question Answeringcited by 7
  11. 2024W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answeringcited by 6
  12. 2024Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Modelscited by 4
8 more
  1. 2024Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Modelscited by 4
  2. 2024Improving Zero-shot LLM Re-Ranker with Risk Minimizationcited by 3
  3. 2023Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answeringcited by 2
  4. 2024Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Modelscited by 2
  5. 2026Efficient Multi-objective Prompt Optimization via Pure-exploration Banditscited by 2
  6. 2026Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learningcited by 1
  7. 2025QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effortcited by 0
  8. 2026iPOE: Interpretable Prompt Optimization via Explanationscited by 0