Proceedings of the AAAI Symposium Series最新文献_第8页

ASMR: Aggregated Semantic Matching Retrieval Unleashing Commonsense Ability of LLM through Open-Ended Question Answering ASMR：聚合语义匹配检索通过开放式问题解答释放 LLM 的常识能力

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31195

Pei-Ying Lin, Erick Chandra, Jane Yung-jen Hsu

{"title":"ASMR: Aggregated Semantic Matching Retrieval Unleashing Commonsense Ability of LLM through Open-Ended Question Answering","authors":"Pei-Ying Lin, Erick Chandra, Jane Yung-jen Hsu","doi":"10.1609/aaaiss.v3i1.31195","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31195","url":null,"abstract":"Commonsense reasoning refers to the ability to make inferences, draw conclusions, and understand the world based on general knowledge and commonsense. Whether Large Language Models (LLMs) have commonsense reasoning ability remains a topic of debate among researchers and experts. When confronted with multiple-choice commonsense reasoning tasks, humans typically rely on their prior knowledge and commonsense to formulate a preliminary answer in mind. Subsequently, they compare this preliminary answer to the provided choices, and select the most likely choice as the final answer. We introduce Aggregated Semantic Matching Retrieval (ASMR) as a solution for multiple-choice commonsense reasoning tasks. To mimic the process of humans solving commonsense reasoning tasks with multiple choices, we leverage the capabilities of LLMs to first generate the preliminary possible answers through open-ended question which aids in enhancing the process of retrieving relevant answers to the question from the given choices. Our experiments demonstrate the effectiveness of ASMR on popular commonsense reasoning benchmark datasets, including CSQA, SIQA, and ARC (Easy and Challenge). ASMR achieves state-of-the-art (SOTA) performance with a peak of +15.3% accuracy improvement over the previous SOTA on SIQA dataset.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Fast and Slow: A Redux of Levels of Learning in General Autonomous Intelligent Agents 学习的快与慢：通用自主智能代理的学习水平再论

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31279

Shiwali Mohan, John E. Laird

引用次数: 0

Responsible Integration of Large Language Models (LLMs) in Navy Operational Plan Generation 负责将大型语言模型 (LLM) 整合到海军作战计划生成中

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31179

Simon Kapiamba, H. Fouad, Ira S. Moskowitz

引用次数: 0

Framework for Federated Learning and Edge Deployment of Real-Time Reinforcement Learning Decision Engine on Software Defined Radio 软件无线电实时强化学习决策引擎的联合学习和边缘部署框架

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31218

Jithin Jagannath

{"title":"Framework for Federated Learning and Edge Deployment of Real-Time Reinforcement Learning Decision Engine on Software Defined Radio","authors":"Jithin Jagannath","doi":"10.1609/aaaiss.v3i1.31218","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31218","url":null,"abstract":"Machine learning promises to empower dynamic resource allocation requirements of Next Generation (NextG) wireless networks including 6G and tactical networks. Recently, we have seen the impact machine learning can make on various aspects of wireless networks. Yet, in most cases, the progress has been limited to simulations and/or relies on large processing units to run the decision engines as opposed to deploying it on the radio at the edge. While relying on simulations for rapid and efficient training of deep reinforcement learning (DRL) may be necessary, it is key to mitigate the sim-real gap while trying to improve the generalization capability. To mitigate these challenges, we developed the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet Gym), an open-source architecture designed for accelerating the deployment of novel DRL for NextG wireless networks. To demonstrate its impact, we tackled the problem of distributed frequency and power allocation while emphasizing the generalization capability of DRL decision engine. The end-to-end solution was implemented on the GPU-embedded software-defined radio and validated using over-the-air evaluation. To the best of our knowledge, these were the first instances that established the feasibility of deploying DRL for optimized distributed resource allocation for next-generation of GPU-embedded radios.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks 法律硕士能否回答投资银行问题？使用领域调整函数提高法律硕士在知识密集型分析任务中的表现

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31191

Nicholas Harvel, F. B. Haiek, Anupriya Ankolekar, David James Brunner

{"title":"Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks","authors":"Nicholas Harvel, F. B. Haiek, Anupriya Ankolekar, David James Brunner","doi":"10.1609/aaaiss.v3i1.31191","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31191","url":null,"abstract":"Large Language Models (LLMs) can increase the productivity of general-purpose knowledge work, but accuracy is a concern, especially in professional settings requiring domain-specific knowledge and reasoning. To evaluate the suitability of LLMs for such work, we developed a benchmark of 16 analytical tasks representative of the investment banking industry. We evaluated LLM performance without special prompting, with relevant information provided in the prompt, and as part of a system giving the LLM access to domain-tuned functions for information retrieval and planning. Without access to functions, state-of-the-art LLMs performed poorly, completing two or fewer tasks correctly. Access to appropriate domain-tuned functions yielded dramatically better results, although performance was highly sensitive to the design of the functions and the structure of the information they returned. The most effective designs yielded correct answers on 12 out of 16 tasks. Our results suggest that domain-specific functions and information structures, by empowering LLMs with relevant domain knowledge and enabling them to reason in domain-appropriate ways, may be a powerful means of adapting LLMs for use in demanding professional settings.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting Machine Learning Bias: Predicting Medical Denials 利用机器学习的偏差：预测医疗拒绝率

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31181

Stephen Russell, Fabio Montes Suros, Ashwin Kumar

引用次数: 0

AI for Social Good Education at Hispanic Serving Institutions 西语裔服务机构的人工智能社会公益教育

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31259

Yu Chen, Gabriel Granco, Yunfei Hou, Heather Macias, Frank A. Gomez

引用次数: 0

Modeling Human-Like Acquisition of Language and Concepts 模拟人类学习语言和概念的过程

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31275

Peter Lindes, Steven Jones

{"title":"Modeling Human-Like Acquisition of Language and Concepts","authors":"Peter Lindes, Steven Jones","doi":"10.1609/aaaiss.v3i1.31275","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31275","url":null,"abstract":"Humans acquire language and related concepts in a trajectory over a lifetime. Concepts for simple interaction with the world are learned before language. Later, words are learned to name these concepts along with structures needed to represent larger meanings. Eventually, language advances to where it can drive the learning of new concepts. Throughout this trajectory a language processing capability uses architectural mechanisms to process language using the knowledge already acquired. We assume that this growing body of knowledge is made up of small units of form-meaning mapping that can be composed in many ways, suggesting that these units are learned incrementally from experience. In prior work we have built a system to comprehend human language within an autonomous robot using knowledge in such units developed by hand. Here we propose a research program to develop the ability of an artificial agent to acquire this knowledge incrementally and autonomously from its experience in a similar trajectory. We then propose a strategy for evaluating this human-like learning system using a large benchmark created as a tool for training deep learning systems. We expect that our human-like learning system will produce better task performance from training on only a small subset of this benchmark.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Image Generation Through Swiping 通过轻扫生成个性化图像

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31238

Yuto Nakashima

引用次数: 0

Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs 使用知识图谱对机器学习分类器进行基于规则的解释

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31200

Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou

{"title":"Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs","authors":"Orfeas Menis Mastromichalakis, Edmund Dervakos, A. Chortaras, G. Stamou","doi":"10.1609/aaaiss.v3i1.31200","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31200","url":null,"abstract":"The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that has lately gained a lot of traction. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier escaping the constraints of using the features of raw data as a means to express the explanations, providing a promising solution to the problem of the understandability of explanations. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel theoretical framework for representing explanations of its operation, in the form of query-based rules expressed in the terminology of the knowledge graph. This allows for explaining opaque black-box classifiers, using terminology and information that is independent of the features of the classifier and its domain of application, leading to more understandable explanations but also allowing the creation of different levels of explanations according to the final end-user.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0