Zobrazeno 1 - 10
of 19 114
pro vyhledávání: '"Baral, A"'
Autor:
Uddin, Md Nayem, Saeidi, Amir, Handa, Divij, Seth, Agastya, Son, Tran Cao, Blanco, Eduardo, Corman, Steven R., Baral, Chitta
This paper introduces UnSeenTimeQA, a novel time-sensitive question-answering (TSQA) benchmark that diverges from traditional TSQA benchmarks by avoiding factual and web-searchable queries. We present a series of time-sensitive event scenarios decoup
Externí odkaz:
http://arxiv.org/abs/2407.03525
Autor:
Schmidt, Stefano, Caudill, Sarah, Creighton, Jolien D. E., Tsukada, Leo, Ray, Anarya, Adhicary, Shomik, Baral, Pratyusava, Baylor, Amanda, Cannon, Kipp, Cousins, Bryce, Ewing, Becca, Fong, Heather, George, Richard N., Godwin, Patrick, Hanna, Chad, Harada, Reiko, Huang, Yun-Jing, Huxford, Rachael, Joshi, Prathamesh, Kennington, James, Kuwahara, Soichiro, Li, Alvin K. Y., Magee, Ryan, Meacher, Duncan, Messick, Cody, Morisaki, Soichiro, Mukherjee, Debnandini, Niu, Wanting, Pace, Alex, Posnansky, Cort, Sachdev, Surabhi, Sakon, Shio, Singh, Divya, Shah, Urja, Tapia, Ron, Tsutsui, Takuya, Ueno, Koh, Viets, Aaron, Wade, Leslie, Wade, Madeline
Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily mis-aligned spins in LIGO and Virgo data taken during the third observing run. Our target syste
Externí odkaz:
http://arxiv.org/abs/2406.17832
Autor:
Patel, Nisarg, Kulkarni, Mohith, Parmar, Mihir, Budhiraja, Aashna, Nakamura, Mutsumi, Varshney, Neeraj, Baral, Chitta
As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like multi-step logical reasoning. Existing logical reasoning evaluation be
Externí odkaz:
http://arxiv.org/abs/2406.17169
Autor:
Varshney, Neeraj, Raj, Satyam, Mishra, Venkatesh, Chatterjee, Agneet, Sarkar, Ritika, Saeidi, Amir, Baral, Chitta
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitation pertinent to 'hallucination' in their output. Recent research has fo
Externí odkaz:
http://arxiv.org/abs/2406.05494
Reasoning about actions and change (RAC) has historically driven the development of many early AI challenges, such as the frame problem, and many AI disciplines, including non-monotonic and commonsense reasoning. The role of RAC remains important eve
Externí odkaz:
http://arxiv.org/abs/2406.04046
This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not entirely correct. The motivation behind this exploration stems from th
Externí odkaz:
http://arxiv.org/abs/2406.03827
Autor:
Anantheswaran, Ujjwala, Gupta, Himanshu, Scaria, Kevin, Verma, Shreyas, Baral, Chitta, Mishra, Swaroop
Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To address this, we propose a prompting framework that generates adversarial va
Externí odkaz:
http://arxiv.org/abs/2406.15444
Autor:
Wu, John F., Hyk, Alina, McCormick, Kiera, Ye, Christine, Astarita, Simone, Baral, Elina, Ciuca, Jo, Cranney, Jesse, Field, Anjalie, Iyer, Kartheik, Koehn, Philipp, Kotler, Jenn, Kruk, Sandor, Ntampaka, Michelle, O'Neill, Charles, Peek, Joshua E. G., Sharma, Sanjib, Yunus, Mikaeel
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currentl
Externí odkaz:
http://arxiv.org/abs/2405.20389
Large Language Models (LLMs) perform well across diverse tasks, but aligning them with human demonstrations is challenging. Recently, Reinforcement Learning (RL)-free methods like Direct Preference Optimization (DPO) have emerged, offering improved s
Externí odkaz:
http://arxiv.org/abs/2405.16681
Domain Generalization (DG) is a challenging task in machine learning that requires a coherent ability to comprehend shifts across various domains through extraction of domain-invariant features. DG performance is typically evaluated by performing ima
Externí odkaz:
http://arxiv.org/abs/2405.15961