Papers with ASM
Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers (2025.naacl-short)
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| Challenge: | ASM classifiers are designed to moderate content on social media platforms and serve as guardrails that prevent Large Language Models (LLMs) from being fine-tuned on unsafe inputs. |
| Approach: | They examine the fairness and robustness of four widely-used, closed-source ASM classifiers: OpenAI Moderation API, Perspective API, Google Cloud Natural Language (GCNL) API, and Clarifai API. |
| Outcome: | The classifiers do not unfairly classify content belonging to minority groups as unsafe compared to those belonging to majority groups and their behavior remains robust and consistent across similar inputs. |
MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation (2025.acl-long)
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Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang, Xinyao Zhang, Pengwei Wang, Jing Zhang, Zhongyuan Wang, Shanghang Zhang, Renjing Xu
| Challenge: | Vision-language navigation (VLN) is a key task in Embodied AI . traditional approaches rely on historical observations as spatio-temporal contexts for decision making . |
| Approach: | They propose a vision-language navigation model that leverages an annotation system to replace historical frames. |
| Outcome: | The proposed model can be used as a new memory representation method in vision-language navigation . it can be applied to simulated and real-world environments, and it is validated by experiments . |
Efficient Active Learning with Adapters (2024.findings-emnlp)
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| Challenge: | Existing studies show that distilled versions of pretrained models are not always available. |
| Approach: | They propose to use distilled versions of successor models as acquisition models to reduce the training cost of the model. |
| Outcome: | The proposed approach reduces the training cost of the model and does not cause the acquisition-successor mismatch (ASM) problem. |