Papers with Symbolic Knowledge Distillation
Argumentation and Judgement Factors: LLM-based Discovery and Application in Insurance Disputes (2026.eacl-long)
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| Challenge: | In this paper, we focus on finding legal factors for a specific case type under consideration . we propose a multi-step approach for discovering a list of AJFs for . a given case type. |
| Approach: | They propose a multi-step approach for discovering a list of AJFs for a given case type . they construct and evaluate the discovered list on two different types of cases . |
| Outcome: | The proposed approach is based on a set of relevant legal documents and a large-scale LLM. |
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)
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Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
| Challenge: | Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards. |
| Approach: | They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model. |
| Outcome: | The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
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Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation (2022.emnlp-main)
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| Challenge: | a new framework for sentence summarization is available that can be trained reference-free . a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios is obtained . |
| Approach: | They propose a framework for sentence summarization that can be trained reference-free . they propose 'referee' that iteratively distills latent knowledge into better models . |
| Outcome: | The proposed framework outperforms existing models in the use of explicit examples from teacher models without compromising the quality of the summarization. |