Dual-View Training for Instruction-Following Information Retrieval

Hugging Face Daily Papers Papers
information-retrieval instruction-following dual-view-training data-synthesis retrieval-systems

Summary

A dual-view data synthesis method using polarity reversal boosts instruction-following retrieval performance by 45% on the FollowIR benchmark.

Instruction-following information retrieval (IF-IR) studies retrieval systems that must not only find documents relevant to a query, but also obey explicit user constraints such as required attributes, exclusions, or output preferences. However, most retrievers are trained primarily for semantic relevance and often fail to distinguish documents that match the topic from those that satisfy the instruction. We propose a dual-view data synthesis strategy based on polarity reversal: given a query, a document that is relevant under the instruction, and a hard negative that matches the query but violates the instruction, we prompt an LLM to generate a complementary instruction under which the two documents swap relevance labels. By presenting the same document pair under complementary instructions that invert their relevance labels, the training signal forces the retriever to reconsider the same candidate set through the instruction, rather than relying on fixed topical cues. On a 305M-parameter encoder, our method improves performance on the FollowIR benchmark by 45%, surpassing general-purpose embedding models of comparable or larger scale. Through head-to-head comparisons at matched data budgets, we further show that data diversity and instruction supervision play complementary roles: the former preserves general retrieval quality, while the latter improves instruction sensitivity. These results highlight the value of targeted data synthesis for building retrieval systems that are both broadly capable and instruction-aware.
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Paper page - Dual-View Training for Instruction-Following Information Retrieval

Source: https://huggingface.co/papers/2604.18845

Abstract

A dual-view data synthesis approach using polarity reversal enhances retrieval systems’ ability to follow instructions by training models to distinguish between topic-relevant and instruction-compliant documents.

Instruction-following information retrieval(IF-IR) studiesretrieval systemsthat must not only find documents relevant to a query, but also obeyexplicit user constraintssuch asrequired attributes,exclusions, oroutput preferences. However, most retrievers are trained primarily for semantic relevance and often fail to distinguish documents that match the topic from those that satisfy the instruction. We propose a dual-viewdata synthesisstrategy based onpolarity reversal: given a query, a document that is relevant under the instruction, and a hard negative that matches the query but violates the instruction, we prompt anLLMto generate acomplementary instructionunder which the two documents swaprelevance labels. By presenting the same document pair undercomplementary instructions that invert theirrelevance labels, thetraining signalforces the retriever to reconsider the same candidate set through the instruction, rather than relying on fixed topical cues. On a 305M-parameterencoder, our method improves performance on theFollowIR benchmarkby 45%, surpassinggeneral-purpose embedding modelsof comparable or larger scale. Through head-to-head comparisons at matched data budgets, we further show thatdata diversityandinstruction supervisionplay complementary roles: the former preserves general retrieval quality, while the latter improves instruction sensitivity. These results highlight the value of targeteddata synthesisfor buildingretrieval systemsthat are both broadly capable and instruction-aware.

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