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LLMs Encode Relevance Internally as Layer-Wise Cross-Lingual Signal

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated how large language models (LLMs) internally represent query-document relevance, moving beyond just evaluating their output scores. They discovered that relevance is encoded as a layer-wise signal within LLMs, becoming more linearly accessible in the middle to later transformer layers. This internal representation can sometimes outperform generated relevance judgments and better preserve system rankings, suggesting a distinction between how LLMs internally understand relevance and how they express it. The study also found partial cross-language transferability of this relevance signal, though it remains weaker than within-language decoding. AI

IMPACT Provides insights into LLM reasoning for information retrieval, potentially improving model interpretability and relevance assessment.

RANK_REASON Academic paper detailing novel research findings on LLM internal representations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs Encode Relevance Internally as Layer-Wise Cross-Lingual Signal

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Academic paper detailing novel research findings on LLM internal representations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Gianluca Demartini ·

    LLMs Encode Relevance as a Layer-Wise Cross-Lingual Signal

    Large language models (LLMs) are increasingly used in information retrieval (IR) pipelines as relevance judges and re-rankers. Yet most analyses remain output-centric, evaluating generated labels or scores while offering limited insight into how relevance is represented inside th…

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