The Single Warehouse Multiple Traveling Salesman Problem (SDMTSP) is an extension of the Traveling Salesman Problem (TSP), widely used in fields such as logistics distribution and transportation planning. This article proposes a new method for solving SDMTSP based on the optimized Sand Cat Swarm Optimization (SCSO). Building on the traditional Sand Cat Algorithm, interval point operations, the pheromone mechanism from the Ant Colony Algorithm (ACA), and an improved t-adaptive mutation operation are introduced to enhance the algorithm's performance in solving multiple traveling salesman problems. Simulation experiment results show that the improved Sand Cat Algorithm performs well under the conditions of N=100, NSalesmen=4, NminTour=5, α=0, β=0.1, and δ=0.9. With 300 repeated iterations, the algorithm performed well on multiple standard test problems and effectively reduced the total travel distance.
Content-based sensor search is a core application in the Internet of Things (IoT), where target sensors can be quickly found by predicting the current output of the sensors. Due to the lack of update mechanism, the established prediction model in existing search architectures will gradually become unavailable in the highly dynamic IoT environment. Hence, a dual prediction structure based on edge intelligence is proposed in this article, to maintain the performance of the prediction model with minimum communication cost in long-term prediction. To implement our architecture, a more effective online learning algorithm is proposed to update prediction models online combined with our proposed adaptive window pattern clustering (AWPC) algorithm. Meanwhile, based on the dual prediction scheme (DPS), a mechanism is deployed on edge sensors to achieve transfer decision making in our architecture, where edge computing is performed to achieve selectively reporting data. With the designed architecture, about 76.56% of the communication energy consumption could be saved while achieving a 95.47% average prediction accuracy in continuous long-term prediction.
Abstract Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing work has to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on most metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.
Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing works have to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on all metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community. Real-life scenario dialogue summarization has a wide potential application prospect in chat-bot and personal assistant. It contains unique challenges such as special discourse structure, coreference, pragmatics, and social common sense, which require specific representation learning technologies to deal with. We carefully annotate a large-scale dialogue summarization dataset based on multiple public dialogue corpus, opening the door to all kinds of summarization models.
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.
We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different methods to improve the performance of dialogue summarization. Although there is a great improvement over the baseline models regarding automatic evaluation metrics, such as Rouge scores, we find that there is a salient gap between model generated outputs and human annotated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion metrics are in need.
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