papers

Papers that I am reading, have read, or will read

Research papers are more than printed pages; they are portals into someone else’s thinking. As Zora Neale Hurston once said, “Research is formalized curiosity. It is poking and prying with a purpose.” And as Albert Szent-Györgyi reminds us, “Research is to see what everybody else has seen, and to think what nobody else has thought.”

Reasoning Papers

• Explaining Answers with Entailment Trees - Dalvi et al., EMNLP 2023
• Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning - Tafjord et al., EMNLP 2022
• Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic - Weir et al., EMNLP 2024

Neuro-Symbolic Papers

• Learning to Compose Neural Networks for Question Answering - Andreas et al., NAACL 2016
• ADAPT: As-Needed Decomposition and Planning with Language Models - Prasad et al., NAACL Findings 2024
• code2vec: Learning Distributed Representations of Code - Alon et al., POPL 2019
• Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning - Theodoropoulos et al., CoNLL 2021
• Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations - Lee et al., Findings of ACL 2020
• EventRAG: Enhancing LLM Generation with Event Knowledge Graphs - Yang et al., ACL 2025
• Augmenting Neural Networks with First-order Logic - Li and Srikumar, ACL 2019
• Logical Transformers: Infusing Logical Structures into Pre-Trained Language Models - Wang et al., Findings of ACL 2023
• A Logic-Driven Framework for Consistency of Neural Models - Li et al., EMNLP 2019
• Logically Consistent Language Models via Neuro-Symbolic Integration - Calanzone et al., ICLR 2025
• Harnessing Deep Neural Networks with Logic Rules - Hu et al., ACL 2016
• Symbolic Knowledge Distillation: from General Language Models to Commonsense Models - West et al., NAACL 2022
• Autoregressive Structured Prediction with Language Models - Liu et al., EMNLP Findings 2022