Retrieval Sensitivity to Identity Signals in Queries

arXiv:2609.36534 · cs.CL, cs.CY, cs.IR · Submitted 2026-09-29 · Read on arXiv

cs.CL, cs.CY, cs.IR

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Andrewtcr/bias-ret

Terminology

• RAG — 11× in this paper · explained in 3 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• African American Language — 6× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Knowledge Discovery and Data Mining — 3× in this paper
• Linear probe — 3× in this paper · explained in 2 episodes
• White Mainstream English — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• F1 Score — 2× in this paper · explained in 3 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks
• BM25S: Orders of magnitude faster lexical search via eager sparse scoring
• Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts
• Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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