While agents' learning is data-driven, sampling from millions

Neural Information Processing Systems http://nips.cc/

Since the rapid expansion of large language models (LLMs), people have begun to rely on them for information retrieval. While traditional search engines display ranked lists of sources shaped by search engine optimization (SEO), advertising, and personalization, LLMs typically provide a synthesized response that feels singular and authoritative. While both approaches carry risks of bias and omission, LLMs may amplify the effect by collapsing multiple perspectives into one answer, reducing users ability or inclination to compare alternatives. This concentrates power over information in a few LLM vendors whose systems effectively shape what is remembered and what is overlooked. As a result, certain narratives, individuals or groups, may be disproportionately suppressed, while others are disproportionately elevated. Over time, this creates a new threat: the gradual erasure of those with limited digital presence, and the amplification of those already prominent, reshaping collective memory. To address these concerns, this paper presents a concept of the Right To Be Remembered (RTBR) which encompasses minimizing the risk of AI-driven information omission, embracing the right of fair treatment, while ensuring that the generated content would be maximally truthful.

While agents' learning is data-driven, sampling from millions

Neural Information Processing Systems http://nips.cc/

We sincerely thank all reviewers for the insightful comments and feedback on our work of learning from failure (LfF). We do not interpret this as a "true" trade-off, as debiasing does not degrade the model's Instead, we view the apparent underperformance as a result of "not utilizing a (delusional) spurious correlation." Following R1's suggestion, we additionally test ReBias [2] (SOT A among This is also consistent with our claim that LfF is not "domain-specific" However, this consistency may not hold depending on the definition of "domain." Hence, we deeply resonate with R2's concern, and we will further clarify the type of knowledge used by LfF and For example, we will modify L2-5 in the abstract by "In this work, we propose a new algorithm utilizing a However, we only use the LfF's yes/no type of knowledge for choosing one of the attributes as an undesired Following R2's suggestion, we further verify Our LfF combination rule achieves 74.01% We will add more discussions and experiments in the final draft.

It is of crucial importance to assess damages promptly and accurately in humanitarian assistance and disaster response (HADR). Current deep learning approaches struggle to generalize effectively due to the imbalance of data classes, scarcity of moderate damage examples, and human inaccuracy in pixel labeling during HADR situations. To accommodate for these limitations and exploit state-of-the-art techniques in vision-language models (VLMs) to fuse imagery with human knowledge understanding, there is an opportunity to generate a diversified set of image-based damage data effectively. Our initial experimental results suggest encouraging data generation quality, which demonstrates an improvement in classifying scenes with different levels of structural damage to buildings, roads, and infrastructures.