Papers with DREAM
DREAM: Improving Situational QA by First Elaborating the Situation (2022.naacl-main)
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| Challenge: | Cognitive science has long promoted the formation of mental models as central to understanding and question-answering. |
| Approach: | They train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about and then provide those elaborations as additional context to a question-answering (QA) model. |
| Outcome: | The proposed model is able to create better scene elaborations than a representative state-of-the-art, zero-shot model. |
DREAM: Deployment of Recombination and Ensembles in Argument Mining (2023.emnlp-main)
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| Challenge: | Current approaches to Argument Mining (AM) take a holistic view of the overall pipeline. |
| Approach: | They propose a framework that allows for the (automated) combination of AM components instead of all-new solutions. |
| Outcome: | The proposed framework outperforms the best single systems in terms of accuracy measured by an AM benchmark. |
DREAM: Deep Research Evaluation with Agentic Metrics (2026.acl-long)
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Elad Ben Avraham, ChangHao Li, Ron Dorfman, Roy Ganz, Oren Nuriel, Amir Dudai, Aviad Aberdam, Noah Flynn, Elman Mansimov, Aditya Kalyanpur, Ron Litman
| Challenge: | Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis . static evaluators lack the tool-use capabilities required to assess temporal validity and factual correctness . |
| Approach: | They propose a framework that instantiates the principle of capability parity by making evaluation agentic. |
| Outcome: | The proposed framework is more sensitive to factual decay than existing benchmarks . large language models increasingly support autonomous, tool-using agents . |
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)
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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu
| Challenge: | Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data. |
| Approach: | They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs. |
| Outcome: | The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback. |
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving (2025.emnlp-main)
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Chuxue Cao, Mengze Li, Juntao Dai, Jinluan Yang, Zijian Zhao, Shengyu Zhang, Weijie Shi, Chengzhong Liu, Sirui Han, Yike Guo
| Challenge: | Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas, but their effectiveness in complex mathematical reasoning involving multi-step FOL deductions remains under-explored. |
| Approach: | They propose a self-adaptive solution that enhances the Diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct their proofs. |
| Outcome: | The proposed model improves diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct proofs. |
Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement (2025.findings-emnlp)
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| Challenge: | Existing methods require full-modality data during training phase or require explicit annotations to detect missing modalities. |
| Approach: | They propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework that directs selective reconstruction of missing or underperforming modalities. |
| Outcome: | The proposed framework outperforms several baseline and state-of-the-art models on three benchmark datasets. |