Limitations, Ethical Considerations, and More: Everything You Need to Know About WikiWebQuestions

Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Related Work Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset Implementation Experiments Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors 8 Conclusion We have created a new high-quality benchmark, WikiWebQuestions, for large knowledge-base question answering. The dataset is based on the popular WebQuestionsSP dataset with natural questions, annotated with SPARQL for Wikidata. We establish a first, strong baseline of 65% answer accuracy and 72% F1 score for WikiWebQuestions. This is achieved by fine-tuning LLaMA with a few-shot training data set using a SPARQL query format modified for semantic parsing. We show that we can reduce the hallucination of large language models like GPT-3 by grounding it with a semantic parser. For the dev set of WikiWebQuestions, this combination approach provides useful information for 96% of the questions in the dev set of the benchmark. More importantly, it generates verifiable answers for 76% of the questions. Limitations While applications of large language models seem to expand every day, this paper mainly focuses on factoid question answering. Long-form text generation, for example, is outside the scope of the experiments of this paper, but the methodology described here may be extended to this setting in the future. Even though knowledge bases are an important source of facts, a large portion of the knowledge available in digital form (e.g. Wikipedia, news articles, etc.), is not organized into knowledge bases. As such, the results of this paper can be considered complementary to the larger body of fact-checking research based on free text. Our semantic parser can be used to verify answers from LLMs. However, this additional round of running the semantic parser and querying Wikidata increase the response latency, which may be noticeable by end-users of such systems. All of our datasets and experiments are conducted for English. Expanding to other languages, while possible (Moradshahi et al., 2020) are outside the scope of this work. Our experiments were performed using GPT-3 (davinci-002) as that was what we had access to when we started the project. Undoubtedly, the later LLMs will produce better results. Nonetheless, the need to have verifiable results based on live database accesses will remain. Ethical Considerations LLMs are used by millions of people everyday. We hope that this line of work will help make them more reliable for everyone, mitigating some of their potential downsides, and giving users access to more accurate information. Our use of Wikidata will enable future researchers and developers to connect their systems with a large, diverse and live knowledge graph that is updated every day. We do not anticipate any harm resulting from the methods introduced in this work. We did not crowdsource any datasets for this paper, as the questions are converted from a previous dataset and all the re-annotation and analysis is done by the authors. To conduct experiments in this paper, we used an estimated total of 60 NC96ads-A100 GPU hours on Microsoft Azure. Each finetuning experiment takes roughly 3 hours, and we conducted roughly 20 experiments to arrive at the results in this paper. Acknowledgements This work is supported in part by the National Science Foundation, the Alfred P. Sloan Foundation, the Verdant Foundation, Microsoft Azure AI credit, KDDI, JPMorgan Chase, and the Stanford HumanCentered Artificial Intelligence (HAI) Institute. We also thank the reviewers for their valuable comments and suggestions. 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Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Authors: Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Semantic Parsing for Wikidata Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset WikiWebQuestions (WWQ) Dataset Implementation Implementation Experiments Experiments Experiment with QALD-7 Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors A. Examples of Recovering from Entity Linking Errors 8 Conclusion We have created a new high-quality benchmark, WikiWebQuestions, for large knowledge-base question answering. The dataset is based on the popular WebQuestionsSP dataset with natural questions, annotated with SPARQL for Wikidata. We establish a first, strong baseline of 65% answer accuracy and 72% F1 score for WikiWebQuestions. This is achieved by fine-tuning LLaMA with a few-shot training data set using a SPARQL query format modified for semantic parsing. We show that we can reduce the hallucination of large language models like GPT-3 by grounding it with a semantic parser. For the dev set of WikiWebQuestions, this combination approach provides useful information for 96% of the questions in the dev set of the benchmark. More importantly, it generates verifiable answers for 76% of the questions. Limitations While applications of large language models seem to expand every day, this paper mainly focuses on factoid question answering. Long-form text generation, for example, is outside the scope of the experiments of this paper, but the methodology described here may be extended to this setting in the future. Even though knowledge bases are an important source of facts, a large portion of the knowledge available in digital form (e.g. Wikipedia, news articles, etc.), is not organized into knowledge bases. As such, the results of this paper can be considered complementary to the larger body of fact-checking research based on free text. Our semantic parser can be used to verify answers from LLMs. However, this additional round of running the semantic parser and querying Wikidata increase the response latency, which may be noticeable by end-users of such systems. All of our datasets and experiments are conducted for English. Expanding to other languages, while possible (Moradshahi et al., 2020) are outside the scope of this work. Our experiments were performed using GPT-3 (davinci-002) as that was what we had access to when we started the project. Undoubtedly, the later LLMs will produce better results. Nonetheless, the need to have verifiable results based on live database accesses will remain. Ethical Considerations LLMs are used by millions of people everyday. We hope that this line of work will help make them more reliable for everyone, mitigating some of their potential downsides, and giving users access to more accurate information. Our use of Wikidata will enable future researchers and developers to connect their systems with a large, diverse and live knowledge graph that is updated every day. We do not anticipate any harm resulting from the methods introduced in this work. We did not crowdsource any datasets for this paper, as the questions are converted from a previous dataset and all the re-annotation and analysis is done by the authors. To conduct experiments in this paper, we used an estimated total of 60 NC96ads-A100 GPU hours on Microsoft Azure. Each finetuning experiment takes roughly 3 hours, and we conducted roughly 20 experiments to arrive at the results in this paper. Acknowledgements This work is supported in part by the National Science Foundation, the Alfred P. Sloan Foundation, the Verdant Foundation, Microsoft Azure AI credit, KDDI, JPMorgan Chase, and the Stanford HumanCentered Artificial Intelligence (HAI) Institute. We also thank the reviewers for their valuable comments and suggestions. References Shengnan An, Bo Zhou, Zeqi Lin, Qiang Fu, Bei Chen, Nanning Zheng, Weizhu Chen, and Jian-Guang Lou. 2023. Skill-based few-shot selection for in-context learning. Aseem Arora, Shabbirhussain Bhaisaheb, Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff. 2023. Adapt and decompose: Efficient generalization of text-to-sql via domain adapted leastto-most prompting. Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, and Andrea Pierleoni. 2022. ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 209–220, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics. Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. 2023. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on Freebase from question-answer pairs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1533–1544, Seattle, Washington, USA. Association for Computational Linguistics. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD ’08, page 1247–1250, New York, NY, USA. Association for Computing Machinery. Giovanni Campagna, Rakesh Ramesh, Silei Xu, Michael Fischer, and Monica S. Lam. 2017. Almond: The architecture of an open, crowdsourced, privacy-preserving, programmable virtual assistant. In Proceedings of the 26th International Conference on World Wide Web - WWW ’17, pages 341–350, New York, New York, USA. ACM Press. Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, and Monica S. Lam. 2019. Genie: A generator of natural language semantic parsers for virtual assistant commands. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019, page 394–410, New York, NY, USA. Association for Computing Machinery. Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, and Hanwang Zhang. 2022a. KQA pro: A dataset with explicit compositional programs for complex question answering over knowledge base. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6101–6119, Dublin, Ireland. Association for Computational Linguistics. Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, and Jinghui Xiao. 2022b. Program transfer for answering complex questions over knowledge bases. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8128–8140, Dublin, Ireland. Association for Computational Linguistics. Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, and Andrew McCallum. 2021. Casebased reasoning for natural language queries over knowledge bases. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9594–9611, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Dennis Diefenbach, Kamal Singh, and Pierre Maret. 2017. Wdaqua-core0: A question answering component for the research community. In Semantic Web Evaluation Challenge, pages 84–89. Springer. Li Dong, Furu Wei, Ming Zhou, and Ke Xu. 2015. Question answering over Freebase with multi-column convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 260–269, Beijing, China. Association for Computational Linguistics. Jerome Goddard. 2023. Hallucinations in chatgpt: A cautionary tale for biomedical researchers. The American Journal of Medicine. 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