Papers with downstream reasoning tasks
C2DLM: Causal Concept-Guided Diffusion Large Language Models (2026.findings-acl)
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Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, Kun Kuang
| Challenge: | Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities. |
| Approach: | They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts. |
| Outcome: | The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. |
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension (2022.acl-long)
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| Challenge: | Procedural Multimodal Documents organize textual instructions and corresponding images step by step. |
| Approach: | They propose a novel temporal-modal entity Graph for comprehending PMDs . they propose encoding and reasoning modules to capture textual and visual entities . |
| Outcome: | The proposed model can capture textual and visual entities and trace their temporal-modal evolution. |
Unveiling Internal Reasoning Modes in LLMs: A Deep Dive into Latent Reasoning vs. Factual Shortcuts with Attribute Rate Ratio (2025.emnlp-main)
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Yiran Yang, Haifeng Sun, Jingyu Wang, Qi Qi, Zirui Zhuang, Huazheng Wang, Pengfei Ren, Jing Wang, Jianxin Liao
| Challenge: | Existing research in multi-hop questions has identified two reasoning modes, but has not investigated how these modes differ during inference. |
| Approach: | They propose a classification metric that compares latent reasoning and factual shortcuts in multi-hop questions. |
| Outcome: | The proposed metric achieves 90% accuracy on the proposed datasets and demonstrates effectiveness in RAG conflict scenarios. |
Self-contradictory reasoning evaluation and detection (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive reasoning ability, but many downstream reasoning tasks focus on performance-wise evaluation. |
| Approach: | They define and assess the Self-Contra rate across three datasets and delve into finer-grained categories of Self-contra reasoning. |
| Outcome: | The proposed model can detect self-contra reasoning with a 52.2% F1 score, much lower than for humans. |