Customer-obsessed science


Research areas
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April 11, 2025Novel three-pronged approach combines claim-level evaluations, chain-of-thought reasoning, and classification of hallucination error types.
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Featured news
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2025While retrieval-augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or irrelevant information. A common detection strategy involves prompting the LLM again to assess whether its response is grounded in the retrieved evidence, but this approach is costly. Alternatively, lightweight natural language
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OOPSLA 20252025Software updates, including bug repair and feature additions, are frequent in modern applications but they often leave test suites outdated, resulting in undetected bugs and increased chances of system failures. A recent study by Meta revealed that 14%-22% of software failures stem from outdated tests that fail to reflect changes in the codebase. This highlights the need to keep tests in sync with code
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NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning, Environmental Science and Technology2025Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product’s entire lifecycle, from raw material extraction to end-of-life. Measuring the emissions outside of a product owner’s control is challenging, and practitioners rely on emission factors (
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2025Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image, a textual description of desired modifications, and the target image, which are expensive and time-consuming to acquire. The scarcity of CIR datasets has led to zero-shot approaches utilizing synthetic triplets or leveraging vision-language
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2025Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question
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