Papers with symbolic methods
Modeling Content and Context with Deep Relational Learning (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing frameworks for combining neural and symbolic representations are limited to simple relational learning tasks. |
| Approach: | They propose a declarative framework for specifying deep relational models that integrates expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
| Outcome: | The proposed framework integrates with expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Walk-based models have shown their advantages in knowledge graph reasoning but are limited by their representations and generalizability. |
| Approach: | They propose a walk-based model that leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk- based agents. |
| Outcome: | Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability. |
CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding (2023.findings-acl)
Copied to clipboard
| Challenge: | Story generation and understanding has seen a surge in neurosymbolic work . symbolic methods are expensive and require a lot of time and expertise . |
| Approach: | They use Code-LLMs to bootstrap the use of symbolic methods for story understanding . they show that they can beat current LLM techniques on pre-existing stories with minimal hand engineering . |
| Outcome: | The proposed system beats state-of-the-art structured LLM techniques on pre-existing story understanding tasks with minimal hand engineering. |
Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have shown strong performance in many reasoning benchmarks, but lack robust planning or symbolic abstractions. |
| Approach: | They propose to synthesize high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. |
| Outcome: | The proposed method can be trained on high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale using Monte Carlo estimation. |