OBJECTIVES: This study explores the use of advanced natural language processing (NLP) techniques to enhance food classification and dietary analysis using raw text input from a diet tracking app. MATERIALS AND METHODS: The study was conducted in 3 stages: data collection, framework development, and application. Data were collected from a 12-week randomized controlled trial (RCT: NCT04259632), in which participants recorded their meals in free-text format using the myCircadianClock app. Only de-identified data were used. We developed nutrition-focused retrieval-augmented generation (NutriRAG), an NLP framework that uses a retrieval-augmented generation approach to enhance food classification from free-text inputs. The framework retrieves relevant examples from a curated database and then leverages large language models, such as GPT-4, to classify user-recorded food items into predefined categories without fine-tuning. NutriRAG was then applied to data from the RCT, which included 77 adults with obesity recruited from the Twin Cities metro area and randomized into 3 intervention groups: time-restricted eating (TRE, 8-hs eating window), caloric restriction (CR, 15% reduction), and unrestricted eating. RESULTS: NutriRAG significantly enhanced classification accuracy and helped to analyze dietary habits, as noted by the retrieval-augmented GPT-4 model achieving a micro-F1 score of 82.24. Both interventions showed dietary alterations: CR participants ate fewer snacks and sugary foods, while TRE participants reduced nighttime eating. CONCLUSION: By using artificial intelligence, NutriRAG marks a substantial advancement in food classification and dietary analysis of nutritional assessments. The findings highlight NLP's potential to personalize nutrition and manage diet-related health issues, suggesting further research to expand these models for wider use.

Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KnowNet a visualization system that integrates LLMs with Knowledge Graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KnowNet extracts triples (e.g., entities and their relations) from LLM outputs and maps them into the validated information and supported evidence in external KGs. For structured exploration, KnowNet provides next-step recommendations based on the neighborhood of the currently explored entities in KGs, aiming to guide a comprehensive understanding without overlooking critical aspects. To enable reasoning with both the structured data in KGs and the unstructured outputs from LLMs, KnowNet conceptualizes the understanding of a subject as the gradual construction of graph visualization. A progressive graph visualization is introduced to monitor past inquiries, and bridge the current query with the exploration history and next-step recommendations. We demonstrate the effectiveness of our system via use cases and expert interviews.

OBJECTIVE: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness. MATERIALS AND METHODS: Our study was conducted in two phases. Initially, we analyzed a range of demonstration components vital for LLMs' biomedical data capabilities, including task descriptions and examples, experimenting with various combinations. Subsequently, we introduced the LLM instruction-example adaptive prompting (LEAP) framework, including instruction adaptive tuning, example adaptive tuning, and instruction-example adaptive tuning methods. This framework aims to systematically investigate both adaptive task descriptions and adaptive examples within the demonstration. We assessed the performance of the LEAP framework on the DDI, ChemProt, and BioRED datasets, employing LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. RESULTS: Our findings indicated that Instruction + Options + Example and its expanded form substantially improved F1 scores over the standard Instruction + Options mode for zero-shot LLMs. The LEAP framework, particularly through its example adaptive prompting, demonstrated superior performance over conventional instruction tuning across all models. Notably, the MedLLAMA_13B model achieved an exceptional F1 score of 95.13 on the ChemProt dataset using this method. Significant improvements were also observed in the DDI 2013 and BioRED datasets, confirming the method's robustness in sophisticated data extraction scenarios. CONCLUSION: The LEAP framework offers a compelling strategy for enhancing LLM training strategies, steering away from extensive fine-tuning towards more dynamic and contextually enriched prompting methodologies, showcasing in biomedical relation extraction.

Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). With the development of deep learning, language models have achieved excellent results. However, these models still cannot answer complex questions. Currently, researchers often utilize structured knowledge, such as knowledge bases (KBs), as external knowledge by directly extracting triples to enhance the results of machine reading. Although they can support certain background knowledge, the triples are limited to the interrelationships among entities or words. Unlike structured knowledge, unstructured knowledge is rich and extensive. However, these methods ignore unstructured knowledge resources, such as Wikipedia. In addition, the effect of combining the two types of knowledge is still not known. In this study, we first attempt to explore the usefulness of combining them. We introduce a fusion mechanism into a rich knowledge fusion layer (RKF) to obtain more useful and relevant knowledge from different external knowledge resources. Further to promote interaction among different types of knowledge, a bi-matching layer is added. We propose the RKF-NET framework based on BERT, and our experimental results demonstrate the effectiveness of two classic datasets: SQuAD1.1 and the Easy-Challenge (ARC).

The WSNs are mainly to monitor various types of sensing content. In the automated control system, administration centers (ACs) often send notifications to a set of nodes meeting given content ranges to implement specific actions. For instance, we notify the sensing devices with sensing temperatures greater than 35 to turn on the cooling system. At present, the transmission of notification messages is mostly based on node identification, which is isolated from sensing contents and unable to accurately locate nodes meeting the sensing content requirements. In this paper, we generalize two types of sensing contents: the continuous values and the discrete values, and a content‐based efficient message transmission mechanism (CEMT) is proposed for the typical tree‐like topologies in WSNs. Thus, a highly effective notifying method for content‐based multicast and anycast messages is designed, which accurately sends notification messages to nodes whose sensing values belong to the given range. Sometimes, multiple sensing types of nodes are mixed together to construct a net topology, offering underlying transport services to each other. At this point, CEMT builds a specialized logical tree for the same type of nodes, and notification messages are transmitted along the logical tree. CEMT reduces the transmission time, bandwidth, and number of processing nodes when a content‐based message is sent, and it takes less storage of content routing entries in nodes.

Clouds play an important role for creating realistic images of outdoor scenes. Many methods have been proposed for modeling and rendering clouds. Among these methods, Dobashi et al. proposed a simple but efficient cloud rendering algorithm based on splatting algorithm and take into account single scattering in couds. This algorithm has been used in many papers to generate realistic clouds images. However, in images generated by using Dobashi's method, lattice like artifact can be easily perceived. This paper describes a method to improve the quality of cloud image generated by dobashi method. We perturb the positions of cloud particles and resample the density at perturbed position within neighboring meatballs to remove the regular pattern from regular space lattice structure and adding noise to texture of billboard used in the original method.