Papers with fine-grained comprehension
CoreGaze: Core Subgraph-Driven Visual Gaze Diffusion for Training-Free Referring Multimodal Large Language Models (2026.acl-long)
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| Challenge: | Existing methods rely on extensive fine-tuning to mitigate attention distraction, leading to redundant outputs or hallucinations. |
| Approach: | They propose a training-free framework that simulates human visual gaze diffusion for fine-grained comprehension by combining a sparse semantic graph with a core subgraph with amplified initial influence. |
| Outcome: | The proposed framework simulates human visual gaze diffusion for fine-grained comprehension. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts (2025.emnlp-main)
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Yuho Lee, Jiaqi Deng, Nicole Hee-Yeon Kim, Hyangsuk Min, Taewon Yun, Minjeong Ban, Kim Yul, Hwanjun Song
| Challenge: | HAMLET is a framework for evaluating the long-context comprehension of large language models. |
| Approach: | They propose a framework for evaluating the long-context comprehension of large language models . HAMLET structures key information of source texts into a three-level hierarchy . |
| Outcome: | HAMLET achieves 90% agreement with expert judgments while reducing evaluation cost by up to 25. |