BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences

• URL: http://arxiv.org/abs/2607.27366v1
• Date: Wed, 29 Jul 2026 18:22:50 GMT
• Title: BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
• Abstract summary: We propose BridgeAlign, among the first preference-alignment pipelines for broad social sciences disciplines.<n>We show that BridgeAlign can achieve the best average across 17 benchmarks against 11 strong baselines.
• Score: 52.67755020436915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS), where nuanced quality judgments matter more than objective correctness. This makes preference alignment a natural paradigm for broad HSS tasks. Yet existing methods are either costly or not tailored to broad HSS disciplines. We thus propose BridgeAlign, among the first preference-alignment pipelines for broad HSS disciplines, with three phases: i) Seed Curation: curating HSS seed documents from web corpora via heuristic/LLM-based filtering and text refinement; ii) Preference Data Synthesis: generating preference triplets via persona-based instruction inversion with Q&A consistency checks; iii) Preference Optimization: moving beyond naive human-vs-model heuristics by first grounding preferences in HSS quality rubric, then generating transitional responses via controlled quality degradation to form near-boundary preference pairs for finer-grained quality discrimination. Aligning over 210k synthetic preference samples, BridgeAlign enables Qwen3-8B to achieve the best average across 17 benchmarks against 11 strong baselines; importantly, leading on both human-preference and knowledge-based capabilities at once, with no trade-off between them, as supported by extensive experiments and contextualized by existing theories.

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