# Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction

arXiv: https://arxiv.org/abs/2601.15037 · HTML: https://mlsanity.com/paper/2601.15037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 30 of 31)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs](https://mlsanity.com/paper/2410.02811/) (2024)
- [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://mlsanity.com/paper/1902.00172/) (2019)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models](https://mlsanity.com/paper/2108.12472/) (2021)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2403.19881/) (2024)
- [Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs](https://mlsanity.com/paper/2410.20321/) (2024)

## Abstract (arXiv)

Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas. Large Language Models (LLMs) have advanced ORTE toward a prompt-driven paradigm through powerful in-context learning. However, adapting their extraction behavior to varying open-domain contexts remains challenging. Existing methods typically rely on manually crafted prompts that remain fixed across inputs, despite substantial variation in linguistic expressions and contextual structures. This mismatch may lead to unsupported triplets, while the absence of ground-truth annotations makes such deficiencies difficult to identify and correct. Moreover, free-form relation generation produces non-canonical relation surface forms, undermining knowledge graph consistency. To address these challenges, we propose Knowledge Restoration-driven Prompt Optimization (KRPO), a framework for label-free target-corpus adaptation. KRPO restores extracted triplets into textual statements and evaluates their semantic consistency with the source inputs, deriving intrinsic feedback without gold annotations. This feedback is transformed into natural-language optimization guidance for batch-wise prompt optimization and adaptation. KRPO further introduces a Memory-augmented Relation Canonicalizer that aligns free-form relations with a dynamically updated schema memory, improving relation consistency. Experiments on three ORTE benchmarks with multiple LLM backbones demonstrate strong overall performance, with KRPO achieving the best average F1 score across the evaluated settings.