# Making Implicit Premises Explicit in Logical Understanding of Enthymemes

arXiv: https://arxiv.org/abs/2603.06114 · HTML: https://mlsanity.com/paper/2603.06114/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://mlsanity.com/paper/1708.01425/) (2017)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Neuro-symbolic Commonsense Social Reasoning](https://mlsanity.com/paper/2303.08264/) (2023)

## Built on by (most cited first, 2 of 2)

- [Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty](https://mlsanity.com/paper/2608.18820/) (2026)
- [Identifying Implicit Premises for Logical Reconstruction of Argument Graphs](https://mlsanity.com/paper/2608.18821/) (2026)

## Abstract (arXiv)

Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language processing (NLP) methods for handling enthymemes can potentially identify enthymemes in text but they do not decode their underlying logic, whereas logic-based approaches for handling them assume a knowledgebase with sufficient formulae that can be used to decode them via abduction. There is therefore a lack of a systematic method for translating textual components of an enthymeme into a logical argument and generating the logical formulae required for their decoding, and thereby showing logical entailment. To address this, we propose a pipeline that integrates: (1) a large language model (LLM) to generate intermediate implicit premises based on the explicit premise and claim; (2) another LLM to translate the natural language into logical formulas; and (3) a neuro-symbolic reasoner based on a SAT solver to determine entailment. We evaluate our pipeline on two enthymeme datasets, demonstrating promising performance in selecting the correct implicit premise, as measured by precision, recall, F1-score, and accuracy.