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pro vyhledávání: '"Wu, Chien-Sheng"'
Despite the recent proliferation of large language models (LLMs), their training recipes -- model architecture, pre-training data and optimization algorithm -- are often very similar. This naturally raises the question of the similarity among the res
Externí odkaz:
http://arxiv.org/abs/2407.16604
LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like Needle-in-a-Haystack lack complexity. In this work, we
Externí odkaz:
http://arxiv.org/abs/2407.01370
Autor:
Agarwal, Divyansh, Fabbri, Alexander R., Risher, Ben, Laban, Philippe, Joty, Shafiq, Wu, Chien-Sheng
Prompt leakage poses a compelling security and privacy threat in LLM applications. Leakage of system prompts may compromise intellectual property, and act as adversarial reconnaissance for an attacker. A systematic evaluation of prompt leakage threat
Externí odkaz:
http://arxiv.org/abs/2404.16251
Ideal summarization models should generalize to novel summary-worthy content without remembering reference training summaries by rote. However, a single average performance score on the entire test set is inadequate in determining such model competen
Externí odkaz:
http://arxiv.org/abs/2311.09458
Autor:
Liu, Yixin, Fabbri, Alexander R., Chen, Jiawen, Zhao, Yilun, Han, Simeng, Joty, Shafiq, Liu, Pengfei, Radev, Dragomir, Wu, Chien-Sheng, Cohan, Arman
While large language models (LLMs) can already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task settings is less studied. Therefore, we benchmark LLMs on instruction control
Externí odkaz:
http://arxiv.org/abs/2311.09184
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited. In this paper, we propose the FlipFlop experiment: in
Externí odkaz:
http://arxiv.org/abs/2311.08596
Making big purchases requires consumers to research or consult a salesperson to gain domain expertise. However, existing conversational recommender systems (CRS) often overlook users' lack of background knowledge, focusing solely on gathering prefere
Externí odkaz:
http://arxiv.org/abs/2310.17749
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. However, standard chat-based conversational interfaces do not support transparency and verifiability of t
Externí odkaz:
http://arxiv.org/abs/2309.15337
Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However, evaluating objectively the creativity of a piece of writing is challenging. Inspired by the Torrance Test of Creative
Externí odkaz:
http://arxiv.org/abs/2309.14556
Autor:
Huang, Kung-Hsiang, Laban, Philippe, Fabbri, Alexander R., Choubey, Prafulla Kumar, Joty, Shafiq, Xiong, Caiming, Wu, Chien-Sheng
Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse information dispersed across multiple articles about an event remains undere
Externí odkaz:
http://arxiv.org/abs/2309.09369