Papers with abductive reasoning
AbductionRules: Training Transformers to Explain Unexpected Inputs (2022.findings-acl)
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| Challenge: | AbductionRules is a set of natural language datasets designed to train and test generalisable abduction over natural-language knowledge bases. |
| Approach: | They propose to train and test generalisable abduction over natural-language knowledge bases by using natural language datasets to fine tune pre-trained Transformers. |
| Outcome: | The proposed models learned generalisable abduction techniques but also exploited the structure of the datasets. |
CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models (2023.findings-eacl)
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| Challenge: | Recent advances in foundation language models have shown the efficacy of pre-trained models across diverse QA tasks. |
| Approach: | They propose a multi-task benchmark for evaluating causality-aware language models to unify causal QA research. |
| Outcome: | The proposed model outperforms single-task fine-tuned models on the CALM-Bench tasks. |
ALERT: Adapt Language Models to Reasoning Tasks (2023.acl-long)
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Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, Asli Celikyilmaz
| Challenge: | Large language models have shown increasing in-context learning capabilities with scaling up the model and data sizes. |
| Approach: | They propose a benchmark and suite of analyses to evaluate reasoning skills of large language models. |
| Outcome: | The proposed model compares pre-trained and fine-tuned models on tasks that require reasoning skills to solve. |
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation (2024.acl-long)
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| Challenge: | Abductive reasoning is the process of making educated guesses to provide explanations for observations. |
| Approach: | They propose a task of complex logical hypothesis generation to generate a complex logique hypothesis that can explain a set of observations. |
| Outcome: | The proposed model generates logical hypotheses closer to the reference hypothesis, but not better on unseen observations. |
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code (2023.findings-acl)
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| Challenge: | entailment a) |
| Approach: | entailment : We want to explore whether Code-LLMs with code prompts are better . encoding a code prompt is better than text-only LLMs, they say . |
| Outcome: | entailment : Our results show that Code-LLMs with code prompts are better compared to text-only LLMs. |
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
| Outcome: | The proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task. |
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations (2023.acl-long)
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| Challenge: | Existing approaches for abductive reasoning in natural language processing rely on manual supervision. |
| Approach: | They propose an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context. |
| Outcome: | The proposed approach outperforms or is comparable to knowledge-augmented zero-shot methods on a diverse set of abductive reasoning datasets. |
Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification (2025.findings-acl)
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John Dougrez-Lewis, Mahmud Elahi Akhter, Federico Ruggeri, Sebastian Löbbers, Yulan He, Maria Liakata
| Challenge: | Large Language Models (LLMs) have shown remarkable proficiency in complex tasks where reasoning capabilities are paramount. |
| Approach: | They propose a framework to break down claims into atomic reasoning types needed for verification. |
| Outcome: | The proposed framework breaks down claims into atomic reasoning types needed for verification. |
Knowledge Vector of Logical Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Logical reasoning is a central capability in LLMs, but understanding their abilities remains poorly understood. |
| Approach: | They propose to refine the knowledge representations of each reasoning type in LLMs to encourage complementarity . they propose to use complementary loss and subspace constraint loss to enhance complementarities . |
| Outcome: | The proposed framework encourages complementarity between the different types of reasoning in LLMs. |