mlsanityData to 7 Oct 2026

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

arXiv 2311.06503 ↗First posted 11 Nov 2023cs.CL cs.AIcited by 28

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especially in ensuring that responses are both accommodating to user requirements and appropriately leveraging domain-specific knowledge bases. They are the two major difficulties for LLM application as vanilla fine-tuning falls short of addressing. Combining these requirements, we conceive of them as the requirement for the model's preference to be harmoniously aligned with humans'. Thus, we introduce Knowledgeable Preference AlignmenT (KnowPAT), which constructs two kinds of preference sets to tackle the two issues. Besides, we design a new alignment objective to align the LLM preference with different human preferences uniformly, aiming to optimize LLM performance in real-world, domain-specific QA settings. Adequate experiments and comprehensive comparisons with 15 baseline methods illustrate that our KnowPAT is a superior pipeline for real-scenario domain-specific QA with LLMs.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 54 of its 86 references that are in the map. First posted on arXiv 11 Nov 2023.

Builds on

54 of 86 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Statistical Rejection Sampling Improves Preference Optimization13 Sep
  2. 2023Making Large Language Models Better Reasoners with Alignment5 Sep
  3. 2023Preference Ranking Optimization for Human Alignment30 Jun
  4. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 50

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
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38 more
  1. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  2. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  3. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignmentcited by 672
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  5. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
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  7. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
  8. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 395
  9. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  10. 2023Who's Harry Potter? Approximate Unlearning in LLMscited by 366
  11. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  12. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
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  15. 2016FVQA: Fact-based Visual Question Answeringcited by 181
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  17. 2023Chain of Hindsight Aligns Language Models with Feedbackcited by 147
  18. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  19. 2022UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graphcited by 100
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  21. 2023PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedbackcited by 69
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  33. 2022Tele-Knowledge Pre-training for Fault Analysiscited by 18
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  35. 2021Billion-scale Pre-trained E-commerce Product Knowledge Graph Modelcited by 13
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  38. 2023Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Educationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 15; 2025: 8; 2026: 4.

28 papers.

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By area:

  1. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  2. 2024Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Surveycited by 50
  3. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  4. 2024NativE: Multi-modal Knowledge Graph Completion in the Wildcited by 23
  5. 2024Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learningcited by 13
  6. 2023Large Knowledge Model: Perspectives and Challengescited by 12
  7. 2024Large Language Models for Social Networks: Applications, Challenges, and Solutionscited by 11
  8. 2024KnowTuning: Knowledge-aware Fine-tuning for Large Language Modelscited by 11
  9. 2024Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completioncited by 10
  10. 2024Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphscited by 10
  11. 2024Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Taskscited by 8
  12. 2025OntoTune: Ontology-Driven Self-training for Aligning Large Language Modelscited by 8
16 more
  1. 2024ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimizationcited by 6
  2. 2024PersLLM: A Personified Training Approach for Large Language Modelscited by 6
  3. 2024DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMscited by 3
  4. 2025Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligningcited by 3
  5. 2025Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answeringcited by 3
  6. 2025The Narrative Continuity Test: A Conceptual Framework for Evaluating Identity Persistence in AI Systemscited by 3
  7. 2024Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinkingcited by 2
  8. 2025Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selectioncited by 2
  9. 2025A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detectioncited by 2
  10. 2025Multimodal Representation Learning Conditioned on Semantic Relationscited by 1
  11. 2024SAG: Style-Aligned Article Generation via Model Collaborationcited by 0
  12. 2025DMA: Online RAG Alignment with Human Feedbackcited by 0
  13. 2026Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledgecited by 0
  14. 2026ReAD: Reinforcement-Guided Capability Distillation for Large Language Modelscited by 0
  15. 2026Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generationcited by 0
  16. 2026ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cuescited by 0

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  3. 2025A Systematic Evaluation of Preference Aggregation in Federated RLHF for Pluralistic Alignment of LLMs
  4. 2024ABC Align: Large Language Model Alignment for Safety & Accuracy
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