Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Feng, Jiazhan"'
While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the nece
Externí odkaz:
http://arxiv.org/abs/2311.07491
Autor:
Feng, Jiazhan, Xu, Ruochen, Hao, Junheng, Sharma, Hiteshi, Shen, Yelong, Zhao, Dongyan, Chen, Weizhu
Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but co
Externí odkaz:
http://arxiv.org/abs/2311.06158
A picture is worth a thousand words, thus, it is crucial for conversational agents to understand, perceive, and effectively respond with pictures. However, we find that directly employing conventional image generation techniques is inadequate for con
Externí odkaz:
http://arxiv.org/abs/2309.15516
Autor:
Feng, Jiazhan, Tao, Chongyang, Geng, Xiubo, Shen, Tao, Xu, Can, Long, Guodong, Zhao, Dongyan, Jiang, Daxin
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLM
Externí odkaz:
http://arxiv.org/abs/2305.07402
Autor:
Xu, Can, Sun, Qingfeng, Zheng, Kai, Geng, Xiubo, Zhao, Pu, Feng, Jiazhan, Tao, Chongyang, Jiang, Daxin
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-comp
Externí odkaz:
http://arxiv.org/abs/2304.12244
Autor:
Feng, Jiazhan, Sun, Qingfeng, Xu, Can, Zhao, Pu, Yang, Yaming, Tao, Chongyang, Zhao, Dongyan, Lin, Qingwei
Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a cura
Externí odkaz:
http://arxiv.org/abs/2211.05719
Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the adoption of pre-trained language models (such as BERT) has led to remark
Externí odkaz:
http://arxiv.org/abs/2110.00159
Publikováno v:
ACL 2019
We study learning of a matching model for response selection in retrieval-based dialogue systems. The problem is equally important with designing the architecture of a model, but is less explored in existing literature. To learn a robust matching mod
Externí odkaz:
http://arxiv.org/abs/1906.04413
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Autor:
Feng, Jiazhan, Tao, Chongyang, Geng, Xiubo, Shen, Tao, Xu, Can, Long, Guodong, Zhao, Dongyan, Jiang, Daxin
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern search engines (SEs). The emergence of large language models (LLMs)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1177f6e7a5403d71a9ad7935b7dd9cec
http://arxiv.org/abs/2305.07402
http://arxiv.org/abs/2305.07402