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pro vyhledávání: '"Abhilasha, .."'
To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at co
Externí odkaz:
http://arxiv.org/abs/2411.11678
Autor:
Rezaei, Keivan, Chandu, Khyathi, Feizi, Soheil, Choi, Yejin, Brahman, Faeze, Ravichander, Abhilasha
Large language models trained on web-scale corpora can memorize undesirable datapoints such as incorrect facts, copyrighted content or sensitive data. Recently, many machine unlearning methods have been proposed that aim to 'erase' these datapoints f
Externí odkaz:
http://arxiv.org/abs/2411.00204
Autor:
Balepur, Nishant, Gu, Feng, Ravichander, Abhilasha, Feng, Shi, Boyd-Graber, Jordan, Rudinger, Rachel
Question answering (QA)-producing correct answers for input questions-is popular, but we test a reverse question answering (RQA) task: given an input answer, generate a question with that answer. Past work tests QA and RQA separately, but we test the
Externí odkaz:
http://arxiv.org/abs/2410.15512
We study the presence of heteronormative biases and prejudice against interracial romantic relationships in large language models by performing controlled name-replacement experiments for the task of relationship prediction. We show that models are l
Externí odkaz:
http://arxiv.org/abs/2410.03996
Tanker-based distribution systems have been prevalent in developing countries to supply clean and pure water in different regions. To efficiently operate such tanker service systems, a large fleet of tanker trucks are required to transport water amon
Externí odkaz:
http://arxiv.org/abs/2408.01184
Autor:
Maheshwari, Abhilasha, Misra, Shamik, Gudi, Ravindra, Subbiah, Senthilmurugan, Laspidou, Chrysi
Tanker water systems play critical role in providing adequate service to meet potable water demands in the face of acute water crisis in many cities globally. Managing tanker movements among the supply and demand sides requires an efficient schedulin
Externí odkaz:
http://arxiv.org/abs/2408.00431
Autor:
Zhao, Wenting, Goyal, Tanya, Chiu, Yu Ying, Jiang, Liwei, Newman, Benjamin, Ravichander, Abhilasha, Chandu, Khyathi, Bras, Ronan Le, Cardie, Claire, Deng, Yuntian, Choi, Yejin
While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that the real-world users of LLMs seek information about. To bridge this gap
Externí odkaz:
http://arxiv.org/abs/2407.17468
Autor:
Brahman, Faeze, Kumar, Sachin, Balachandran, Vidhisha, Dasigi, Pradeep, Pyatkin, Valentina, Ravichander, Abhilasha, Wiegreffe, Sarah, Dziri, Nouha, Chandu, Khyathi, Hessel, Jack, Tsvetkov, Yulia, Smith, Noah A., Choi, Yejin, Hajishirzi, Hannaneh
Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of "unsafe" queries, we posit that the scope of noncompliance should be broadened. We int
Externí odkaz:
http://arxiv.org/abs/2407.12043
Attributing answer text to its source document for information-seeking questions is crucial for building trustworthy, reliable, and accountable systems. We formulate a new task of post-hoc answer attribution for long document comprehension (LDC). Owi
Externí odkaz:
http://arxiv.org/abs/2406.06938
Autor:
Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina, Dziri, Nouha, Bras, Ronan Le, Choi, Yejin
We introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024 tasks carefully selected from over one million human-chatbot conversa
Externí odkaz:
http://arxiv.org/abs/2406.04770