Papers by Zi’ou Zheng
Exploring End-to-End Differentiable Natural Logic Modeling (2020.coling-main)
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| Challenge: | Existing approaches to integrate natural logic with neural networks are brittle and prone to fail in the presence of noise and uncertainty. |
| Approach: | They propose to integrate natural logic with neural networks to create differentiable models that integrate natural reasoning with subsymbolic vector representations and neural components. |
| Outcome: | The proposed model can model monotonicity-based reasoning, compared to baseline models without inductive bias. |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
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| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |
Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are essential for performing complex multi-step reasoning tasks, such as multi-hop reasoning tasks. |
| Approach: | They propose to use large language models to derive structured intermediate proof steps to improve their performance by using examples. |
| Outcome: | The proposed models can derive correct proof steps with in-context learning. |