Papers by Aaron Colak
Memory-QA: Answering Recall Questions Based on Multimodal Memories (2025.emnlp-main)
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Hongda Jiang, Xinyuan Zhang, Siddhant Garg, Rishab Arora, Shiun-Zu Kuo, Jiayang Xu, Aaron Colak, Xin Luna Dong
| Challenge: | Memory-QA is a real-world task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Approach: | They propose a memory-QA task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Outcome: | The proposed solution improves memory recording, compression, storage, and search accuracy over state-of-the-art solutions. |
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)
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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions. |
| Approach: | They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions. |
| Outcome: | The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production. |