# Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

arXiv: https://arxiv.org/abs/2610.10508 · HTML: https://mlsanity.com/paper/2610.10508/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [NV-Retriever: Improving text embedding models with effective hard-negative mining](https://mlsanity.com/paper/2407.15831/) (2024)
- [RAR-b: Reasoning as Retrieval Benchmark](https://mlsanity.com/paper/2404.06347/) (2024)
- [INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models](https://mlsanity.com/paper/2402.14334/) (2024)
- [SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval](https://mlsanity.com/paper/2412.17250/) (2024)
- [F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2603.19223/) (2026)
- [Towards Better Instruction Following Retrieval Models](https://mlsanity.com/paper/2505.21439/) (2025)
- [Do Prompts Reshape Representations? An Empirical Study of Prompting Effects on Embeddings](https://mlsanity.com/paper/2510.19694/) (2025)

## Abstract (arXiv)

Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datasets and studies have examined this setting, showing that the current embedding models often struggle to follow such instructions reliably. In this paper, we study the mechanism of how instructions actually affect the representations of retrieval queries in asymmetric retrieval tasks. We show that models can fail to follow even simple task instructions when query-side distractors are included in the evaluation. We hypothesize that this behavior is driven by the training setup of current embedding models and their evaluation, and show that fine-tuning with added query-side distractors leads to substantial improvements, with minimal effect on other tasks.