Chi Zhang, Yixin Zhu, Guangyuan Jiang, Manjie Xu, Wei Liang, Shiji Xin, Yujia Peng
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jianggy/mewl | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_attributes | Ran | jianggy/mewl/generate_mewl.py code served (permissive licence) · get_code("74cfed5f2dbe5798") |
| random_lexicon | Ran | jianggy/mewl/generate_mewl.py code served (permissive licence) · get_code("831e85c16526d769") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This word learning capability is believed to be the most fundamental building block of multimodal understanding and reasoning. Despite recent advancements in multimodal learning, a systematic and rigorous evaluation is still missing for humanlike word learning in machines. To fill in this gap, we introduce the MachinE Word Learning ( MEWL) benchmark to assess how machines learn word meaning in grounded visual scenes. MEWL covers human's core cognitive toolkits in word learning: cross-situational reasoning, bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot benchmark suite consisting of nine tasks for probing various word learning capabilities. These tasks are carefully designed to be aligned with the children's core abilities in word learning and echo the theories in the developmental literature. By evaluating multimodal and unimodal agents' performance with a comparative analysis of human performance, we notice a sharp divergence in human and machine word learning. We further discuss these differences between humans and machines and call for human-like few-shot word learning in machines.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.00503")
get_code_for_paper("2306.00503")
have("2306.00503")
Connect an agent — have() is free.