Papers with relational
SLR: Automated Synthesis for Scalable Logical Reasoning (2026.acl-long)
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Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting
| Challenge: | Existing benchmarks intended to evaluate reasoning capabilities emphasize deductive reasoning, where conclusions necessarily follow from given premises. |
| Approach: | They propose an end-to-end framework for systematic evaluation and training of Large Language Models via Scalable Logical Reasoning. |
| Outcome: | The proposed framework doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. |
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (N18-2)
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| Challenge: | Existing systems that can understand natural language questions and generate corresponding SQL queries are not able to do this. |
| Approach: | They propose a novel approach which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions. |
| Outcome: | The proposed approach outperforms the prior art on the WikiSQL dataset and can reach 82.6% accuracy, a 17.5% improvement compared to the previous content-sensitive model. |
SymBa: Symbolic Backward Chaining for Structured Natural Language Reasoning (2025.naacl-long)
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| Challenge: | Among different methods for structured reasoning, we focus on backward chaining, where the goal is recursively decomposed into subgoals by searching and applying rules. |
| Approach: | They propose a backward chaining system that integrates a symbolic solver and an LLM to improve the performance of LLM-based reasoning. |
| Outcome: | The proposed system improves deductive, relational, and arithmetic reasoning benchmarks compared to baselines. |
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts (D19-1)
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| Challenge: | gradable adjectives of size are relative, i.e., determined by the context. |
| Approach: | They propose to model how the meaning of gradable adjectives of size can be learned from visually-grounded contexts by using four tasks to determine whether an object is ‘big’ or ‘small’. |
| Outcome: | The proposed model can learn subtending the meaning of size adjectives, but their performance decreases while moving from simple to more complex tasks. |