# An Enhanced Knowledge Injection Model for Commonsense Generation

arXiv: https://arxiv.org/abs/2012.00366 · HTML: https://mlsanity.com/paper/2012.00366/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning](https://mlsanity.com/paper/2404.00213/) (2024)
- [On the Way to LLM Personalization: Learning to Remember User Conversations](https://mlsanity.com/paper/2411.13405/) (2024)
- [Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning](https://mlsanity.com/paper/2404.16807/) (2024)
- [Retrieval Enhanced Model for Commonsense Generation](https://mlsanity.com/paper/2105.11174/) (2021)
- [NAREOR: The Narrative Reordering Problem](https://mlsanity.com/paper/2104.06669/) (2021)
- [Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models](https://mlsanity.com/paper/2109.03892/) (2021)
- [SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation](https://mlsanity.com/paper/2108.06643/) (2021)
- [Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning](https://mlsanity.com/paper/2210.11708/) (2022)
- [Contextualized Scene Imagination for Generative Commonsense Reasoning](https://mlsanity.com/paper/2112.06318/) (2021)
- [When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations](https://mlsanity.com/paper/2505.24449/) (2025)
- [KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation](https://mlsanity.com/paper/2112.08266/) (2021)
- [Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach](https://mlsanity.com/paper/2205.13183/) (2022)
- [KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning](https://mlsanity.com/paper/2109.06704/) (2021)
- [Learning to Predict Concept Ordering for Common Sense Generation](https://mlsanity.com/paper/2309.06363/) (2023)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)
- [Lexical Complexity Controlled Sentence Generation](https://mlsanity.com/paper/2211.14540/) (2022)
- [KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering](https://mlsanity.com/paper/2305.08347/) (2023)
- [DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation](https://mlsanity.com/paper/2403.01954/) (2024)
- [PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation](https://mlsanity.com/paper/2209.07752/) (2022)
- [ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation](https://mlsanity.com/paper/2209.15109/) (2022)
- [KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls](https://mlsanity.com/paper/2510.19316/) (2025)

## Abstract (arXiv)

Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules, namely position indicator and scaling module, into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, and experimental results show that our method significantly improves the performance on all the metrics.