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    • All HBS Web  (3,769)
      • Faculty Publications  (497)

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      • 2024
      • Conference Paper

      Quantifying Uncertainty in Natural Language Explanations of Large Language Models

      By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
      Large Language Models (LLMs) are increasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Recent prompting works claim to elicit intermediate reasoning steps and key tokens that serve as proxy explanations for LLM... View Details
      Keywords: Large Language Model; AI and Machine Learning
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      Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
      • 2023
      • Article

      Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset

      By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
      Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
      Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
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      Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • 2023
      • Article

      Post Hoc Explanations of Language Models Can Improve Language Models

      By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
      Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
      Keywords: AI and Machine Learning; Performance Effectiveness
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      Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • 2023
      • Other Article

      The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications

      By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
      Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
      Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
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      Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • December 2023
      • Article

      When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments

      By: Christian Kaps, Simone Marinesi and Serguei Netessine
      Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
      Keywords: Energy; Renewable Energy
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      Kaps, Christian, Simone Marinesi, and Serguei Netessine. "When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments." Management Science 69, no. 12 (December 2023): 7633–7650.
      • 2023
      • Article

      Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness

      By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
      One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
      Keywords: AI and Machine Learning; Mathematical Methods
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      Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • November 2023
      • Case

      Open Source Machine Learning at Google

      By: Shane Greenstein, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue and James Barnett
      Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
      Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
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      Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
      • November 2023 (Revised March 2024)
      • Case

      Infarm: Betting the (Indoor) Farm on Food Security

      By: Elie Ofek
      In the summer of 2023, the co-founders of Infarm, a controlled environment agriculture (CEA) company, were contemplating a major pivot going forward. While Infarm had successfully shown it could grow over 75 products—mainly herbs, leafy greens and mushrooms—in modular... View Details
      Keywords: Plant-Based Agribusiness; Business Model; Market Entry and Exit; Science-Based Business; Business Strategy; Transition; Agriculture and Agribusiness Industry; Europe; North America; Toronto; Northeastern United States
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      Ofek, Elie. "Infarm: Betting the (Indoor) Farm on Food Security." Harvard Business School Case 524-043, November 2023. (Revised March 2024.)
      • November 2023 (Revised April 2024)
      • Case

      Khanmigo: Revolutionizing Learning with GenAI

      By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean
      Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with... View Details
      Keywords: Technology Adoption; Leading Change; Entrepreneurship; Risk and Uncertainty; Education; AI and Machine Learning; Corporate Social Responsibility and Impact; Education Industry; Technology Industry; United States; San Francisco
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      Sahlman, William A., Allison M. Ciechanover, and Emily Grandjean. "Khanmigo: Revolutionizing Learning with GenAI." Harvard Business School Case 824-059, November 2023. (Revised April 2024.)
      • November–December 2023
      • Article

      Keep Your AI Projects on Track

      By: Iavor Bojinov
      AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
      Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
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      Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
      • November 2023
      • Article

      Open Source Software and Global Entrepreneurship

      By: Nataliya Langburd Wright, Frank Nagle and Shane Greenstein
      This is the first study to consider the relationship between open source software (OSS) and entrepreneurship around the globe. This study measures whether country-level participation on the GitHub OSS platform affects the founding of innovative ventures, and where it... View Details
      Keywords: Entrepreneurship; Applications and Software; Business Ventures; Development Economics; Innovation and Invention; Global Range
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      Wright, Nataliya Langburd, Frank Nagle, and Shane Greenstein. "Open Source Software and Global Entrepreneurship." Art. 104846. Research Policy 52, no. 9 (November 2023).
      • October 2023 (Revised February 2024)
      • Case

      Loris

      By: Shunyuan Zhang, Das Narayandas, Stacy Straaberg and David Lane
      In December 2022, Loris’s executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs)... View Details
      Keywords: Decisions; Growth and Development Strategy; Product Launch; Product Positioning; Business Strategy; Competitive Strategy; Business Startups; AI and Machine Learning; Applications and Software; Marketing Strategy; Sales; Technology Industry; United States
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      Zhang, Shunyuan, Das Narayandas, Stacy Straaberg, and David Lane. "Loris." Harvard Business School Case 524-010, October 2023. (Revised February 2024.)
      • October 2023 (Revised November 2023)
      • Case

      Recycle & Re-Match: The Future of Soccer Turfs

      By: George Serafeim, Lena Duchene and Carlota Moniz
      By August 2023, Re-Match, an artificial turf waste-to-value company, had operations in Denmark and the Netherlands and had recycled over 160,000 tons of waste and plastic fiber. With recent capital injection from the VC firm Verdane and a dual revenue business model,... View Details
      Keywords: Carbon Emissions; Carbon Abatement; Sustainability; Recycling; Waste Management; Technology; Entrepreneurial Management; Business Growth and Maturation; Business Model; Decisions; Energy Conservation; Investment Return; Profit; Technological Innovation; Patents; Growth and Development Strategy; Market Entry and Exit; Digital Platforms; Wastes and Waste Processing; Business Strategy; Competition; Expansion; Technology Adoption; Sports; Environmental Sustainability; Entrepreneurship; Green Technology Industry; Service Industry; Manufacturing Industry; Rubber Industry; Sports Industry; Denmark; Netherlands; France; United States; Pennsylvania; Europe
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      Serafeim, George, Lena Duchene, and Carlota Moniz. "Recycle & Re-Match: The Future of Soccer Turfs." Harvard Business School Case 124-032, October 2023. (Revised November 2023.)
      • 2023
      • Working Paper

      Black-box Training Data Identification in GANs via Detector Networks

      By: Lukman Olagoke, Salil Vadhan and Seth Neel
      Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
      Keywords: Cybersecurity; Copyright; AI and Machine Learning; Analytics and Data Science
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      Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
      • 2023
      • Working Paper

      Causal Interpretation of Structural IV Estimands

      By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
      We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
      Keywords: Mathematical Methods
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      Andrews, Isaiah, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan, and Jesse M. Shapiro. "Causal Interpretation of Structural IV Estimands." NBER Working Paper Series, No. 31799, October 2023.
      • October 2023
      • Article

      Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

      By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
      We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
      Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
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      Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
      • 2023
      • Working Paper

      In-Context Unlearning: Language Models as Few Shot Unlearners

      By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
      Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is... View Details
      Keywords: AI and Machine Learning; Copyright; Information
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      Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
      • September 2023 (Revised September 2024)
      • Case

      IBJ, Inc. (A): Seeking Matrimony in Japan

      By: Ramon Casadesus-Masanell and Akiko Saito
      In March 2020, Shigeru Ishizaka, founder and CEO of IBJ, Inc., Japan's largest marriage matching service provider, faced a critical decision regarding the company’s planned ¥3.5 billion (US$32.8 million) acquisition of competitor ZWEI Co., Ltd. IBJ, founded in 2006,... View Details
      Keywords: Mergers and Acquisitions; Risk and Uncertainty; Business Model; Corporate Strategy; Value
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      Casadesus-Masanell, Ramon, and Akiko Saito. "IBJ, Inc. (A): Seeking Matrimony in Japan." Harvard Business School Case 724-356, September 2023. (Revised September 2024.)
      • September 2023 (Revised January 2024)
      • Case

      AI21 Labs in 2023: Strategy for Generative AI

      By: David Yoffie, Orna Dan and Elena Corsi
      Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text... View Details
      Keywords: Decision Making; AI and Machine Learning; Innovation Strategy; Growth and Development Strategy; Applications and Software; Competitive Strategy; Technology Industry; Israel
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      Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
      • September 2023 (Revised March 2024)
      • Case

      ReMo Energy: Sizing Up Investors

      By: Jeffrey J. Bussgang and Tom Quinn
      In 2023, executives with ReMo Energy (founded 2020) were deciding which size ammonia plant to build as their first project. Their innovative model produced ammonia—useful for making fertilizer and for energy storage—from renewable energy, and they had received funding... View Details
      Keywords: Factories, Labs, and Plants; Business Startups; Cost vs Benefits; Design; Energy Conservation; Energy Generation; Renewable Energy; Venture Capital; Investment Return; Goods and Commodities; Size; Infrastructure; Risk and Uncertainty; Science-Based Business; Commercialization; Technological Innovation; Chemical Industry; Energy Industry; Green Technology Industry; United States; Boston
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      Bussgang, Jeffrey J., and Tom Quinn. "ReMo Energy: Sizing Up Investors." Harvard Business School Case 824-027, September 2023. (Revised March 2024.)
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