BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences

arXiv:2607.27366 · cs.CL · Submitted 2026-08-14 · Read on arXiv

Ru Peng, Haokai Xu, Xijun Gu, Tianyu Zhao, Zhiting Fan, Yawen Zeng, Yihong Zhuang, Jinyang Zhang, Kexin Yang, Jian Wu, Hao Chen, Junyang Lin, Dayiheng Liu, Junbo Zhao

cs.CL

Submitted: 2026-08-14

Updated: 2026-08-18

Code: https://github.com/huggingface/datatrove

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 41× in this paper · explained in 1 episode
• Benchmark — 41× in this paper · explained in 2 episodes
• Core — 32× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Consistency — 13× in this paper · explained in 3 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• Instruction Tuning — 7× in this paper · explained in 2 episodes
• Redundancy — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Lexical Diversity — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Bradley-Terry model — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Emotional Resonance — 1× in this paper · explained in 2 episodes
• Generalized Generalization Model Inference — 1× in this paper
• Hallucination — 1× in this paper · explained in 3 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Program Synthesis with Large Language Models
• Constitutional AI: Harmlessness from AI Feedback
• LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
• Large Language Models as Tool Makers
• ToMBench: Benchmarking Theory of Mind in Large Language Models
• The Llama 3 Herd of Models
• Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Hierarchical Neural Story Generation
• Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
• Scaling Synthetic Data Creation with 1,000,000,000 Personas
• Measuring Massive Multitask Language Understanding
• FastText.zip: Compressing text classification models
• LongForm: Effective Instruction Tuning with Reverse Instructions
• RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
• DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities
• Preference Leakage: A Contamination Problem in LLM-as-a-judge
• Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
• Dual-Difficulty Curriculum Learning for Direct Preference Optimization
• Towards General Text Embeddings with Multi-stage Contrastive Learning

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