Zobrazeno 1 - 10
of 278
pro vyhledávání: '"Neville, Jennifer"'
Autor:
Shi, Taiwei, Wang, Zhuoer, Yang, Longqi, Lin, Ying-Chun, He, Zexue, Wan, Mengting, Zhou, Pei, Jauhar, Sujay, Xu, Xiaofeng, Song, Xia, Neville, Jennifer
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive
Externí odkaz:
http://arxiv.org/abs/2408.15549
Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models. Due to the scarcity of datasets, current methods easily suffer from learning bias. To solve this problem, we embed a Large Language
Externí odkaz:
http://arxiv.org/abs/2407.15351
We explore the use of Large Language Model (LLM-based) chatbots to power recommender systems. We observe that the chatbots respond poorly when they encounter under-specified requests (e.g., they make incorrect assumptions, hedge with a long response,
Externí odkaz:
http://arxiv.org/abs/2406.01633
Autor:
Chen, Qi, Geng, Xiubo, Rosset, Corby, Buractaon, Carolyn, Lu, Jingwen, Shen, Tao, Zhou, Kun, Xiong, Chenyan, Gong, Yeyun, Bennett, Paul, Craswell, Nick, Xie, Xing, Yang, Fan, Tower, Bryan, Rao, Nikhil, Dong, Anlei, Jiang, Wenqi, Liu, Zheng, Li, Mingqin, Liu, Chuanjie, Li, Zengzhong, Majumder, Rangan, Neville, Jennifer, Oakley, Andy, Risvik, Knut Magne, Simhadri, Harsha Vardhan, Varma, Manik, Wang, Yujing, Yang, Linjun, Yang, Mao, Zhang, Ce
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked
Externí odkaz:
http://arxiv.org/abs/2405.07526
Workplace social media platforms enable employees to cultivate their professional image and connect with colleagues in a semi-formal environment. While semi-formal corporate communication poses a unique set of challenges, large language models (LLMs)
Externí odkaz:
http://arxiv.org/abs/2405.04656
Random Projections have been widely used to generate embeddings for various graph learning tasks due to their computational efficiency. The majority of applications have been justified through the Johnson-Lindenstrauss Lemma. In this paper, we take a
Externí odkaz:
http://arxiv.org/abs/2404.10148
Autor:
Schnabel, Tobias, Neville, Jennifer
In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves. In these settings, prompt programs are repeatedly called with different user queries or data instances. A big practical challenge is opt
Externí odkaz:
http://arxiv.org/abs/2404.02319
Autor:
Suri, Siddharth, Counts, Scott, Wang, Leijie, Chen, Chacha, Wan, Mengting, Safavi, Tara, Neville, Jennifer, Shah, Chirag, White, Ryen W., Andersen, Reid, Buscher, Georg, Manivannan, Sathish, Rangan, Nagu, Yang, Longqi
Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like te
Externí odkaz:
http://arxiv.org/abs/2404.04268
Autor:
Wan, Mengting, Safavi, Tara, Jauhar, Sujay Kumar, Kim, Yujin, Counts, Scott, Neville, Jennifer, Suri, Siddharth, Shah, Chirag, White, Ryen W, Yang, Longqi, Andersen, Reid, Buscher, Georg, Joshi, Dhruv, Rangan, Nagu
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and
Externí odkaz:
http://arxiv.org/abs/2403.12173
Autor:
Lin, Ying-Chun, Neville, Jennifer, Stokes, Jack W., Yang, Longqi, Safavi, Tara, Wan, Mengting, Counts, Scott, Suri, Siddharth, Andersen, Reid, Xu, Xiaofeng, Gupta, Deepak, Jauhar, Sujay Kumar, Song, Xia, Buscher, Georg, Tiwary, Saurabh, Hecht, Brent, Teevan, Jaime
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in bo
Externí odkaz:
http://arxiv.org/abs/2403.12388