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| Zineng Tang, Ziyi Yang, Chenguang Zhu, Michael Zeng, 和 Mohit Bansal
Imagine an AI model that can seamlessly …
![An illustration of the KEAR architecture represented by five panels side by side. The first contains an input question—“What is a treat that your dog will enjoy?”—and the answer choices “salad,” “petted,” “affection,” “bone,” and “lots of attention.” The second panel has three boxes, each representing retrieval from a specific knowledge source. A box labeled “Knowledge Graph” has a silhouette of a dog and underneath it and labeled “desires” a silhouette of a dog being petted; a heart representing “affection”; a bone; and clapping hands representing “lots of attention.” A box labeled “relevant questions” has the question “What do dogs like to eat?” and the accompanying answer “Bones.” A boxed labeled “dictionary” contains the definition of “bone”: “a composite material making up the skeleton of most vertebrates.” The third panel, labeled “concatenation with input,” contains the input question followed by “Dog, desires, bone. Dog, desires, lots of attention” followed by the relevant question and finally the dictionary definition of bone. In between each is a separation token [SEP]. The fourth panel is labeled “language model” and contains a quote box labeled “language services,” a cube labeled “model,” and left and right braces punctuation within a circle labeled “language understanding.” The fifth panel is labeled “output” and includes silhouettes of each of the five answer choices. The silhouette of the bone is highlighted in blue, representing the appropriate response.](https://www.microsoft.com/en-us/research/wp-content/uploads/2021/12/1400x788_Common_Sense_still_no_logo-1-480x280.jpg){kind=logo}
| Yichong Xu, Chenguang Zhu, Shuohang Wang, Michael Zeng, 和 Xuedong Huang
KEAR (Knowledgeable External Attention f…