Papers with reasoning problem
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)
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| Challenge: | Existing privacy studies focus on sub-fields, but they focus on a few sub-domains. |
| Approach: | They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations. |
| Outcome: | The proposed checklist covers social identities, private attributes, and existing privacy regulations. |
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning (2023.acl-long)
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| Challenge: | Existing QA models rely on shortcuts to provide the true answer, referred to as disconnected reasoning problem. |
| Approach: | They propose a causal-effect approach that exploits true multi-hop reasoning instead of shortcuts. |
| Outcome: | The proposed method achieves 5.8% higher points of its Supps score on hotpotQA through true multihop reasoning. |
Decomposition-Enhanced Training for Post-Hoc Attributions in Language Models (2026.eacl-long)
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Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami, Ryan A. Rossi, Varun Manjunatha, Roshan Santhosh, Ruiyi Zhang, Soheil Feizi, Nedim Lipka
| Challenge: | Existing methods for extractive QA struggle in multi-hop, abstractive, and semi-extractive settings. |
| Approach: | They propose a method that prompts models to produce answer decompositions as intermediate reasoning steps. |
| Outcome: | The proposed method outperforms existing methods and matches or exceeds state-of-the-art frontier models. |
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)
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| Challenge: | Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks. |
| Approach: | They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms . |
| Outcome: | The proposed method outperforms baselines on three public KBQG datasets. |
Asynchronous Deep Interaction Network for Natural Language Inference (D19-1)
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| Challenge: | Existing methods have framed the reasoning problem as a semantic matching task. |
| Approach: | They propose an asynchronous deep interaction network (ADIN) to deconstruct the reasoning process and implement asynchron and multi-step reasoning. |
| Outcome: | The proposed model outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail. |
Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning (2024.emnlp-main)
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| Challenge: | Existing methods do not differentiate question difficulty when designing prompting methods for them. |
| Approach: | They propose an adaptive method to improve large language models for reasoning problems by measuring question difficulty and tailoring demonstration set construction and difficulty-adapted retrieval strategies. |
| Outcome: | The proposed method shows an absolute improvement of up to 5.5% on arithmetic reasoning, 7.4% on symbolic reasoning, and 2.3% on commonsense reasoning. |
How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects (2026.findings-acl)
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| Challenge: | a number of theories have been proposed to account for content effects in large language models, including the dual-process theory of reasoning, but the mechanisms behind content effects remain unclear. |
| Approach: | They propose to encode validity and plausibility concepts in LLMs by aligning them in representational geometry. |
| Outcome: | The proposed model conflates validity and plausibility, and vice versa. |
Evaluating the Deductive Competence of Large Language Models (2024.naacl-long)
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| Challenge: | Existing large language models have limited abilities to solve deductive reasoning problems . performance differences between conditions do not improve overall performance . |
| Approach: | They investigate whether several large language models can solve a deductive reasoning problem in their conventional form. |
| Outcome: | The proposed models can solve a classic type of deductive reasoning problem in their conventional form. |