Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users' earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.

The quality is a crucial issue for crowd annotations. Answer aggregation is an important type of solution. The aggregated answers estimated from multiple crowd answers to the same instance are the eventually collected annotations, rather than the individual crowd answers themselves. Recently, the capability of Large Language Models (LLMs) on data annotation tasks has attracted interest from researchers. Most of the existing studies mainly focus on the average performance of individual crowd workers; several recent works studied the scenarios of aggregation on categorical labels and LLMs used as label creators. However, the scenario of aggregation on text answers and the role of LLMs as aggregators are not yet well-studied. In this paper, we investigate the capability of LLMs as aggregators in the scenario of close-ended crowd text answer aggregation. We propose a human-LLM hybrid text answer aggregation method with a Creator-Aggregator Multi-Stage (CAMS) crowdsourcing framework. We make the experiments based on public crowdsourcing datasets. The results show the effectiveness of our approach based on the collaboration of crowd workers and LLMs.

Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluated as correct or incorrect, unlike close-ended questions with definitive answers. While large language models (LLMs) have demonstrated strong capabilities across various tasks, they exhibit relatively weaker performance in evaluating answers to open-ended questions. In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. We utilized LLMs to generate multiple evaluation criteria for a question. Subsequently, answers were subjected to pairwise comparisons under each criterion with LLMs, and scores for each answer were calculated in the AHP. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines. Additionally, we explored the impact of the number of criteria, variations in models, and differences in datasets on the results.

Activists in Japan earlier this year accused one of the country's largest robotics manufacturers of profiting off the war in Gaza, accusing it of violating its own company policies in aiding the Israeli defense industry. At a protest outside the headquarters of FANUC Corporation earlier this summer, the Boycott, Divestment, and Sanctions (BDS) protesters demanded the Japanese conglomerate cut off ties with Israel and all the defense companies that contribute to Israel's military. "We also call on FANUC not to be further complicit in genocide, war crimes, and crimes against humanity," Taizo Imano, one of the protest organizers, said in June. Specifically, Imano and the rest of the BDS activists believe Japan is breaching its own export controls. If true, it would significantly alter how Israel acquires high-end machinery for its defense sector.

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning capability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our source code is released at https://github.com/WangXFng/RDRec.

Whether Large Language Models (LLMs) can outperform crowdsourcing on the data annotation task is attracting interest recently. Some works verified this issue with the average performance of individual crowd workers and LLM workers on some specific NLP tasks by collecting new datasets. However, on the one hand, existing datasets for the studies of annotation quality in crowdsourcing are not yet utilized in such evaluations, which potentially provide reliable evaluations from a different viewpoint. On the other hand, the quality of these aggregated labels is crucial because, when utilizing crowdsourcing, the estimated labels aggregated from multiple crowd labels to the same instances are the eventually collected labels. Therefore, in this paper, we first investigate which existing crowdsourcing datasets can be used for a comparative study and create a benchmark. We then compare the quality between individual crowd labels and LLM labels and make the evaluations on the aggregated labels. In addition, we propose a Crowd-LLM hybrid label aggregation method and verify the performance. We find that adding LLM labels from good LLMs to existing crowdsourcing datasets can enhance the quality of the aggregated labels of the datasets, which is also higher than the quality of LLM labels themselves.

Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines or quadratic discriminant analysis. By performing a grid-search-based hyperparameter optimization on 23 feature spaces we can show that all considered classifiers are capable of outperforming the current state-of-the-art neural network-based approaches in terms of total IoU on their best-performing feature spaces. With total and mean IoU values of 0.8751 and 0.7031 compared to 0.70 and 0.5873 as the previous best-reported results, we show that a simple gradient boosting classifier can significantly improve over deep neural network based approaches, despite using less training data. Furthermore, an analysis of the regional distribution of the Sen1Floods11 dataset reveals a problem of spatial imbalance. We show that traditional machine learning models can learn this bias and argue that modified metric evaluations are required to counter artifacts due to spatial imbalance. Lastly, a qualitative analysis shows that this pixel-wise classifier provides highly-precise surface water classifications indicating that a good choice of a feature space and pixel-wise classification can generate high-quality flood maps using optical and SAR data. We make our code publicly available at: https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance

Endangered animal species could be saved from extinction after a new study has shown that mice cloned from freeze-dried cells are able to successfully grow into adults and become parents. Researchers in Japan have used freeze-dried somatic cells – animal cells other than sperm and egg cells – to clone mice. Cloned males and females were able to mate with other normal mice and produce their own healthy offspring. The team's method could bring animal species back to life after they've gone extinct in the wild, as long as their cells have already been'banked'. Researchers in Japan have used freeze-dried somatic cells to clone mice.

Amusement parks, baseball clubs and other entertainment businesses in Japan are increasingly adopting dynamic ticket pricing in a bid to avoid creating crowds amid the COVID-19 pandemic while stabilizing revenue. Those businesses hope that dynamic pricing will help bring in more customers as tickets are cheap on days with low demand. The ticket sales market in Japan in the year ended in February 2021 shrank to a quarter of that of before the pandemic, according to Pia Research Institute, an arm of ticketing agency Pia Corp. Meanwhile, the total value of dynamically priced tickets sold in the country is expected to grow by 1.5-fold to around ¥6.2 billion in the year ending this month from the previous year, according to Dynamic Plus Co., a Mitsui & Co. unit that uses artificial intelligence to offer dynamic pricing services. Under a dynamic pricing plan, prices are changed depending on demand until the day of the event.

Chiba – Researchers in Japan have been conducting experiments using robotics and artificial intelligence to alleviate fruit farmers' reliance on scarce labor while supporting those who are aging and have no successor. Trials are underway in Chiba Prefecture, a major production area for Japanese pears, and Yamanashi Prefecture, the country's main grape-growing region. In spring this year, a consortium made up of the Chiba Prefectural Government, agricultural cooperatives, and other concerns launched a two-year experimental project at pear-growing properties in the cities of Ichikawa and Narita in the prefecture. According to Tokyo-based consulting company NTT Data Institute of Management Consulting Inc., which oversees the experiments, a robot cargo vehicle automatically follows workers as they harvest pears, transporting the fruit to a designated location. An integrated camera shoots photographs of the prepicked pears and surrounding foliage, AI analyzes the data and provides information on the best time for the fruit to be harvested based on its growth.