Papers with MMR
Considering Length Diversity in Retrieval-Augmented Summarization (2025.findings-naacl)
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| Challenge: | Existing methods that require exhaustive exemplar-exemplar relevance comparisons do not consider summary lengths. |
| Approach: | They propose a Diverse Length-aware Maximal Marginal Relevance algorithm to better control summary lengths. |
| Outcome: | The proposed algorithm reduces the computational cost and memory consumption while maintaining the same level of informativeness. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering (2024.findings-acl)
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| Challenge: | Existing studies have focused on assessing the model’s overall accuracy without evaluating it on different reasoning cases. |
| Approach: | They propose a novel idea to identify and improve multi-modal multi-hop reasoning in VQA by using two new language prompts to find a reasoning path to reach its answer. |
| Outcome: | The proposed model improves multi-modal multi-hop reasoning in visual question answering (VQA) it finds that the proposed model is easy to answer, simply demanding “single-hop” reasoning, whereas only a few questions require “multi-hop.” |
GAMIC: Graph-Aligned Molecular In-context Learning for Molecule Analysis via LLMs (2025.findings-emnlp)
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| Challenge: | Current methods for retrieving large language models rely on molecule feature similarity, such as Morgan fingerprints, which do not adequately capture the global molecular and atom-binding relationships. |
| Approach: | They propose a self-supervised learning technique that embeds demonstration examples into the input prompt. |
| Outcome: | The proposed technique outperforms simple Morgan-based retrieval methods across tasks by up to 45%. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
Budget-Aware Routing for Long Clinical Text (2026.findings-acl)
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| Challenge: | Long-context capability is now a headline feature of large language models . clinical inputs are long because they are templated, redundant, and stitched from multiple sources. |
| Approach: | They propose a token-constrained subset selection problem with two design choices . they propose heuristics that balance relevance, coverage, diversity and a monotone submodular objective . |
| Outcome: | The proposed model is based on a subset selection problem with two design choices . positional heuristics perform best at low budgets in extractive tasks, while diversity-aware methods improve LLM generation. |