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Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation, leading to poor generalizability and harmfulness of LLMs. However, due to the diversity of dataset biases and the over-optimization problem, previous prior-knowledge-based debiasing methods and fine-tuning-based debiasing methods may not be suitable for current LLMs. To address this issue, we explore combining active learning with the causal mechanisms and propose a casual-guided active learning (CAL) framework, which utilizes LLMs itself to automatically and autonomously identify informative biased samples and induce the bias patterns. Then a cost-effective and efficient in-context learning based method is employed to prevent LLMs from utilizing dataset biases during generation. Experimental results show that CAL can effectively recognize typical biased instances and induce various bias patterns for debiasing LLMs.

Police in Washington state announced two suspects were arrested in connection with the murder of a missing elderly man who was allegedly poisoned with fentanyl by a pair who gained his trust through a dating app. The Mercer Island Police released a statement saying Philip J. Brewer, 32, and Christina Hardy, 47, are facing charges for the murder of Curtis Engeland, 74, by using an elaborate scheme to defraud and murder him. Police said that Brewer and Hardy are believed to have become acquainted with Engeland several months ago and subsequently financially defrauded him. Police also believe the suspects later violently confronted Engeland at his Mercer Island home in the late evening hours of February 23, and used Engeland's vehicle to leave Mercer Island that night. POLICE MADE'A DEAL WITH THE DEVIL' TO UNCOVER LOCATION OF MISSING BLOOD MOUNTAIN HIKER: KILLER WAS'HUNTING' Two suspects were arrested in connection to the homicide of missing Mercer Island resident Curtis Engeland, 74.

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare species in the Pacific Northwest of the United States.