pwc-archive/papers-with-abstracts huggingface dataset:
KemenkeuGPT: Leveraging a Large Language Model on Indonesia's Government Financial Data and Regulations to Enhance Decision Making
Authors: Gilang Fajar Febrian, Grazziela Figueredo
Date: 2024-07-31 00:00:00
Tasks: Benchmarking, Decision Making, Language Modeling, Language Modelling, Large Language Model, Prompt Engineering, RAG, Retrieval, Retrieval-augmented Generation

Data is crucial for evidence-based policymaking and enhancing public services, including those at the Ministry of Finance of the Republic of Indonesia. However, the complexity and dynamic nature of governmental financial data and regulations can hinder decision-making. This study investigates the potential of Large Language Models (LLMs) to address these challenges, focusing on Indonesia's financial data and regulations. While LLMs are effective in the financial sector, their use in the public sector in Indonesia is unexplored. This study undertakes an iterative process to develop KemenkeuGPT using the LangChain with Retrieval-Augmented Generation (RAG), prompt engineering and fine-tuning. The dataset from 2003 to 2023 was collected from the Ministry of Finance, Statistics Indonesia and the International Monetary Fund (IMF). Surveys and interviews with Ministry officials informed, enhanced and fine-tuned the model. We evaluated the model using human feedback, LLM-based evaluation and benchmarking. The model's accuracy improved from 35% to 61%, with correctness increasing from 48% to 64%. The Retrieval-Augmented Generation Assessment (RAGAS) framework showed that KemenkeuGPT achieved 44% correctness with 73% faithfulness, 40% precision and 60% recall, outperforming several other base models. An interview with an expert from the Ministry of Finance indicated that KemenkeuGPT has the potential to become an essential tool for decision-making. These results are expected to improve with continuous human feedback.
eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs
Authors: Xinyi Pan, Daniel Hernández, Philipp Seifer, Ralf Lämmel, Steffen Staab
Date: 2024-07-31 00:00:00
Tasks: Knowledge Graphs, valid

Over the past few years, we have seen the emergence of large knowledge graphs combining information from multiple sources. Sometimes, this information is provided in the form of assertions about other assertions, defining contexts where assertions are valid. A recent extension to RDF which admits statements over statements, called RDF-star, is in revision to become a W3C standard. However, there is no proposal for a semantics of these RDF-star statements nor a built-in facility to operate over them. In this paper, we propose a query language for epistemic RDF-star metadata based on a four-valued logic, called eSPARQL. Our proposed query language extends SPARQL-star, the query language for RDF-star, with a new type of FROM clause to facilitate operating with multiple and sometimes conflicting beliefs. We show that the proposed query language can express four use case queries, including the following features: (i) querying the belief of an individual, (ii) the aggregating of beliefs, (iii) querying who is conflicting with somebody, and (iv) beliefs about beliefs (i.e., nesting of beliefs).
Parallel Strategies for Best-First Generalized Planning
Date: 2024-07-31 00:00:00

In recent years, there has been renewed interest in closing the performance gap between state-of-the-art planning solvers and generalized planning (GP), a research area of AI that studies the automated synthesis of algorithmic-like solutions capable of solving multiple classical planning instances. One of the current advancements has been the introduction of Best-First Generalized Planning (BFGP), a GP algorithm based on a novel solution space that can be explored with heuristic search, one of the foundations of modern planners. This paper evaluates the application of parallel search techniques to BFGP, another critical component in closing the performance gap. We first discuss why BFGP is well suited for parallelization and some of its differentiating characteristics from classical planners. Then, we propose two simple shared-memory parallel strategies with good scaling with the number of cores.
Operator-based semantics for choice programs: is choosing losing? (full version)
Authors: Jesse Heyninck
Date: 2024-07-31 00:00:00

Choice constructs are an important part of the language of logic programming, yet the study of their semantics has been a challenging task. So far, only two-valued semantics have been studied, and the different proposals for such semantics have not been compared in a principled way. In this paper, an operator-based framework allow for the definition and comparison of different semantics in a principled way is proposed.
TRGR: Transmissive RIS-aided Gait Recognition Through Walls
Authors: Yunlong Huang, Junshuo Liu, Jianan Zhang, Tiebin Mi, Xin Shi, Robert Caiming Qiu
Date: 2024-07-31 00:00:00
Tasks: Gait Recognition

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88\% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.
Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components
Date: 2024-07-31 00:00:00

Automation of medical image interpretation could alleviate bottlenecks in diagnostic workflows, and has become of particular interest in recent years due to advancements in natural language processing. Great strides have been made towards automated radiology report generation via AI, yet ensuring clinical accuracy in generated reports is a significant challenge, hindering deployment of such methods in clinical practice. In this work we propose a quality control framework for assessing the reliability of AI-generated radiology reports with respect to semantics of diagnostic importance using modular auxiliary auditing components (AC). Evaluating our pipeline on the MIMIC-CXR dataset, our findings show that incorporating ACs in the form of disease-classifiers can enable auditing that identifies more reliable reports, resulting in higher F1 scores compared to unfiltered generated reports. Additionally, leveraging the confidence of the AC labels further improves the audit's effectiveness.
Human interaction classifier for LLM based chatbot
Date: 2024-07-31 00:00:00

This study investigates different approaches to classify human interactions in an artificial intelligence-based environment, specifically for Applus+ IDIADA's intelligent agent AIDA. The main objective is to develop a classifier that accurately identifies the type of interaction received (Conversation, Services, or Document Translation) to direct requests to the appropriate channel and provide a more specialized and efficient service. Various models are compared, including LLM-based classifiers, KNN using Titan and Cohere embeddings, SVM, and artificial neural networks. Results show that SVM and ANN models with Cohere embeddings achieve the best overall performance, with superior F1 scores and faster execution times compared to LLM-based approaches. The study concludes that the SVM model with Cohere embeddings is the most suitable option for classifying human interactions in the AIDA environment, offering an optimal balance between accuracy and computational efficiency.
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities
Authors: Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
Date: 2024-07-31 00:00:00
Tasks: Diversity, slot-filling, Slot Filling

Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effectively for information collection has always been a critical and challenging task. Recent studies have demonstrated that Large Language Models (LLMs) excel in dialogue, instruction generation, and reasoning, and can significantly enhance the performance of TOD through fine-tuning. However, current datasets primarily cater to user-led systems and are limited to predefined specific scenarios and slots, thereby necessitating improvements in the proactiveness, diversity, and capabilities of TOD. In this study, we present a detailed multi-domain task-oriented data construction process for conversations, and a Chinese dialogue dataset generated based on this process, TransferTOD, which authentically simulates human-computer dialogues in 30 popular life service scenarios. Leveraging this dataset, we trained a model called TransferTOD-7B using full-parameter fine-tuning, showcasing notable abilities in slot filling and questioning. Our work has demonstrated its strong generalization capabilities in various downstream scenarios, significantly enhancing both data utilization efficiency and system performance. The data is released in https://github.com/KongLongGeFDU/TransferTOD.
CEAR: Automatic construction of a knowledge graph of chemical entities and roles from scientific literature
Date: 2024-07-31 00:00:00

Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.
UMMAN: Unsupervised Multi-graph Merge Adversarial Network for Disease Prediction Based on Intestinal Flora
Date: 2024-07-31 00:00:00

The abundance of intestinal flora is closely related to human diseases, but diseases are not caused by a single gut microbe. Instead, they result from the complex interplay of numerous microbial entities. This intricate and implicit connection among gut microbes poses a significant challenge for disease prediction using abundance information from OTU data. Recently, several methods have shown potential in predicting corresponding diseases. However, these methods fail to learn the inner association among gut microbes from different hosts, leading to unsatisfactory performance. In this paper, we present a novel architecture, Unsupervised Multi-graph Merge Adversarial Network (UMMAN). UMMAN can obtain the embeddings of nodes in the Multi-Graph in an unsupervised scenario, so that it helps learn the multiplex association. Our method is the first to combine Graph Neural Network with the task of intestinal flora disease prediction. We employ complex relation-types to construct the Original-Graph and disrupt the relationships among nodes to generate corresponding Shuffled-Graph. We introduce the Node Feature Global Integration (NFGI) module to represent the global features of the graph. Furthermore, we design a joint loss comprising adversarial loss and hybrid attention loss to ensure that the real graph embedding aligns closely with the Original-Graph and diverges from the Shuffled-Graph. Comprehensive experiments on five classical OTU gut microbiome datasets demonstrate the effectiveness and stability of our method. (We will release our code soon.)
Assessing the State of AI Policy
Date: 2024-07-31 00:00:00

The deployment of artificial intelligence (AI) applications has accelerated rapidly. AI enabled technologies are facing the public in many ways including infrastructure, consumer products and home applications. Because many of these technologies present risks either in the form of physical injury, or bias, potentially yielding unfair outcomes, policy makers must consider the need for oversight. Most policymakers, however, lack the technical knowledge to judge whether an emerging AI technology is safe, effective, and requires oversight, therefore policy makers must depend on expert opinion. But policymakers are better served when, in addition to expert opinion, they have some general understanding of existing guidelines and regulations. This work provides an overview [the landscape] of AI legislation and directives at the international, U.S. state, city and federal levels. It also reviews relevant business standards, and technical society initiatives. Then an overlap and gap analysis are performed resulting in a reference guide that includes recommendations and guidance for future policy making.
ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization
Date: 2024-07-31 00:00:00

As a broadly applied technique in numerous optimization problems, recently, local search has been employed to solve Pseudo-Boolean Optimization (PBO) problem. A representative local search solver for PBO is LSPBO. In this paper, firstly, we improve LSPBO by a dynamic scoring mechanism, which dynamically strikes a balance between score on hard constraints and score on the objective function. Moreover, on top of this improved LSPBO , we develop the first parallel local search PBO solver. The main idea is to share good solutions among different threads to guide the search, by maintaining a pool of feasible solutions. For evaluating solutions when updating the pool, we propose a function that considers both the solution quality and the diversity of the pool. Furthermore, we calculate the polarity density in the pool to enhance the scoring function of local search. Our empirical experiments show clear benefits of the proposed parallel approach, making it competitive with the parallel version of the famous commercial solver Gurobi.
An Application of Large Language Models to Coding Negotiation Transcripts
Authors: Ray Friedman, Jaewoo Cho, Jeanne Brett, Xuhui Zhan, Ningyu Han, Sriram Kannan, Yingxiang Ma, Jesse Spencer-Smith, Elisabeth Jäckel, Alfred Zerres, Madison Hooper, Katie Babbit, Manish Acharya, Wendi Adair, Soroush Aslani, Tayfun Aykaç, Chris Bauman, Rebecca Bennett, Garrett Brady, Peggy Briggs, Cheryl Dowie, Chase Eck, Igmar Geiger, Frank Jacob, Molly Kern, Sujin Lee, Leigh Anne Liu, Wu Liu, Jeffrey Loewenstein, Anne Lytle, Li Ma, Michel Mann, Alexandra Mislin, Tyree Mitchell, Hannah Martensen née Nagler, Amit Nandkeolyar, Mara Olekalns, Elena Paliakova, Jennifer Parlamis, Jason Pierce, Nancy Pierce, Robin Pinkley, Nathalie Prime, Jimena Ramirez-Marin, Kevin Rockmann, William Ross, Zhaleh Semnani-Azad, Juliana Schroeder, Philip Smith, Elena Stimmer, Roderick Swaab, Leigh Thompson, Cathy Tinsley, Ece Tuncel, Laurie Weingart, Robert Wilken, Jingjing Yao, Zhi-Xue Zhang
Date: 2024-07-18 00:00:00
Tasks: In-Context Learning, Zero-Shot Learning

In recent years, Large Language Models (LLM) have demonstrated impressive capabilities in the field of natural language processing (NLP). This paper explores the application of LLMs in negotiation transcript analysis by the Vanderbilt AI Negotiation Lab. Starting in September 2022, we applied multiple strategies using LLMs from zero shot learning to fine tuning models to in-context learning). The final strategy we developed is explained, along with how to access and use the model. This study provides a sense of both the opportunities and roadblocks for the implementation of LLMs in real life applications and offers a model for how LLMs can be applied to coding in other fields.
They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models
Authors: Mohammad Saeid Mahdavinejad, Peyman Adibi, Amirhassan Monadjemi, Pascal Hitzler
Date: 2024-07-21 00:00:00
Tasks: Decision Making, Depression Detection

Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns. We address this challenge by introducing ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep leverages prototype learning and the generative power of Large Language Models to provide transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights. Evaluated on five benchmark datasets, ProtoDep achieves near state-of-the-art performance while learning meaningful prototypes. This multi-faceted approach offers significant potential to enhance the reliability and transparency of depression detection on social media, ultimately aiding mental health professionals in delivering more informed care.
Unlocking the Potential: Benchmarking Large Language Models in Water Engineering and Research
Authors: Boyan Xu, Liang Wen, Zihao Li, Yuxing Yang, Guanlan Wu, Xiongpeng Tang, Yu Li, Zihao Wu, Qingxian Su, Xueqing Shi, Yue Yang, Rui Tong, How Yong Ng
Date: 2024-07-22 00:00:00
Tasks: Benchmarking

Recent advancements in Large Language Models (LLMs) have sparked interest in their potential applications across various fields. This paper embarked on a pivotal inquiry: Can existing LLMs effectively serve as "water expert models" for water engineering and research tasks? This study was the first to evaluate LLMs' contributions across various water engineering and research tasks by establishing a domain-specific benchmark suite, namely, WaterER. Herein, we prepared 983 tasks related to water engineering and research, categorized into "wastewater treatment", "environmental restoration", "drinking water treatment and distribution", "sanitation", "anaerobic digestion" and "contaminants assessment". We evaluated the performance of seven LLMs (i.e., GPT-4, GPT-3.5, Gemini, GLM-4, ERNIE, QWEN and Llama3) on these tasks. We highlighted the strengths of GPT-4 in handling diverse and complex tasks of water engineering and water research, the specialized capabilities of Gemini in academic contexts, Llama3's strongest capacity to answer Chinese water engineering questions and the competitive performance of Chinese-oriented models like GLM-4, ERNIE and QWEN in some water engineering tasks. More specifically, current LLMs excelled particularly in generating precise research gaps for papers on "contaminants and related water quality monitoring and assessment". Additionally, they were more adept at creating appropriate titles for research papers on "treatment processes for wastewaters", "environmental restoration", and "drinking water treatment". Overall, this study pioneered evaluating LLMs in water engineering and research by introducing the WaterER benchmark to assess the trustworthiness of their predictions. This standardized evaluation framework would also drive future advancements in LLM technology by using targeting datasets, propelling these models towards becoming true "water expert".
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Authors: Yuxuan Hu, Minghuan Tan, Chenwei Zhang, Zixuan Li, Xiaodan Liang, Min Yang, Chengming Li, Xiping Hu
Date: 2024-07-23 00:00:00
Tasks: Empathetic Response Generation, Response Generation, Retrieval, Semantic Retrieval

Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empathy and affective empathy. The former pertains to the ability to understand and discern the emotional issues and situations of others, while the latter involves the capacity to provide comfort. To enhance one's empathetic abilities, it is essential to develop both these aspects. Therefore, we develop an innovative framework that combines retrieval augmentation and emotional support strategy integration. Our framework starts with the introduction of a comprehensive emotional palette for empathy. We then apply appraisal theory to decompose this palette and create a database of empathetic responses. This database serves as an external resource and enhances the LLM's empathy by integrating semantic retrieval mechanisms. Moreover, our framework places a strong emphasis on the proper articulation of response strategies. By incorporating emotional support strategies, we aim to enrich the model's capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response. Finally, we extract datasets ED and ET from the empathetic dialogue dataset \textsc{EmpatheticDialogues} and ExTES based on dialogue length. Experiments demonstrate that our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives. Our code is released at https://github.com/CAS-SIAT-XinHai/APTNESS.