Xiaofei Xu

Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Language Models (LLMs) offer promise in automating counter-response generation to mitigate misinformation, but a critical challenge lies in their tendency to hallucinate non-factual information. Existing models mainly rely on LLM self-feedback to reduce hallucination, but this approach is computationally expensive. In this paper, we propose MisMitiFact, Misinforma-tion Mitigation grounded in Facts, an efficient framework for generating fact-grounded counter-responses at scale. MisMitiFact generates simple critique feedback to refine LLM outputs, ensuring responses are grounded in evidence. We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites to identify and correct errors in key elements such as numerals, entities, and topics in LLM generations. Experiments show that MisMitiFact generates counter-responses of comparable quality to LLMs' self-feedback while using significantly smaller critique models. Importantly, it achieves ∼5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation. Code and LLM prompt templates are at https://github.com/xxfwin/ MisMitiFact.

Zoned namespace (ZNS) SSDs are emerging storage devices offering low cost, high performance, and software definability. By adopting host-managed zone-based sequential programming, ZNS SSDs effectively eliminate the space overhead associated with on-board DRAM memory and garbage collection. However, while background read refreshing serves as a data protection mechanism in conventional block-interface SSDs, state-of-the-art ZNS SSDs lack read refreshing functionality to guarantee data reliability. Moreover, implementing zonelevel read refreshing in ZNS SSDs incurs significant overhead due to the large volume of valid data movements in a zone, leading to degraded I/O performance. To efficiently enable read refreshing for ZNS SSDs, this paper proposes FineRR-ZNS, a fine-granularity read refreshing mechanism for ZNS SSDs. FineRR-ZNS employs a host-controlled fine-granularity read refreshing scheme that selectively determines block-level read refreshing via metadata remapping. A zone reconstruction method is also designed to retrieve remapped data forming complete data during zone-level RR. Specifically, the remapped data after zone reconstruction are still available and prioritized for read access until their respective blocks need the next RR. Evaluation results show that FineRR-ZNS significantly enhances read refreshing efficiency and I/O throughput compared to zone-level read refreshing implemented in the state-of-the-art ZenFS file system.

Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Models (LLMs). Existing studies in the literature focus only on either textual quality or numerical reasoning and thus are inadequate to address this challenge. In this paper, we propose a novel chain-of-thought framework for using rationales comprising key elements of the Topic, Entities, and Numerical reasoning (TEN) in news articles to enhance the capability for LLMs to generate topic-aligned high-quality texts with precise numerical accuracy. Specifically, a teacher LLM is employed to generate TEN rationales as supervision data, which are then used to teach and fine-tune a student LLM. Our approach teaches the student LLM automatic generation of rationales with enhanced capability for numerical reasoning and topic-aligned numerical headline generation. Experiments show that our approach achieves superior performance in both textual quality and numerical accuracy.

The management of garbage collection poses significant challenges in high-density NAND flash-based SSDs. The introduction of the copyback command aims to expedite the migration of valid data. However, its odd/even constraint causes wasted pages during migrations, limiting the efficiency of garbage collection. Additionally, while full-sequence programming en-hances write performance in high-density SSDs, it increases write granularity and exacerbates the issue of wasted pages. To address the problem of wasted pages, we propose a novel method called CoupledCB, which utilizes coupled blocks to fill up the wasted space in copyback-based garbage collection. By taking into account the access characteristics of the candidate coupled blocks and workloads, we develop a coupled block selection model assisted by logistic regression. Experimental results show that our proposal significantly enhances garbage collection efficiency and 1/O performance compared to state-of-the-art schemes.