Ellie Pavlick
Biographic Data
| ID | 6537085 |
|---|---|
| NAME | Ellie Pavlick |
| GIVEN NAMES | Ellie |
| FAMILY NAME | Pavlick |
| SIGNATURE | PAVLICK E |
| AFFILIATIONS | Brown University |
| ORCID | 0000-0002-7155-5420 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
LLMs model how humans induce logically structured rules
LLMs as models for analogical reasoning
Analogical reasoning — the capacity to identify and map structural relationships between different domains — is fundamental to human cognition and learning. Recent studies have shown that large language models (LLMs) can sometimes match humans in analogical reasoning tasks, opening the possibility that analogical reasoning might emerge from domain-general processes. However, it is still debated whether these emergent capacities are largely superf…
Semantic Structure in Deep Learning
Deep learning has recently come to dominate computational linguistics, leading to claims of human-level performance in a range of language processing tasks. Like much previous computational work, deep learning–based linguistic representations adhere to the distributional meaning-in-use hypothesis, deriving semantic representations from word co-occurrence statistics. However, current deep learning methods entail fundamentally new models of lexical…
Semantic Structure in Deep Learning
Deep learning has recently come to dominate computational linguistics, leading to claims of human-level performance in a range of language processing tasks. Like much previous computational work, deep learning–based linguistic representations adhere to the distributional meaning-in-use hypothesis, deriving semantic representations from word co-occurrence statistics. However, current deep learning methods entail fundamentally new models of lexical…
Semantic Structure in Deep Learning
Deep learning has recently come to dominate computational linguistics, leading to claims of human-level performance in a range of language processing tasks. Like much previous computational work, deep learning–based linguistic representations adhere to the distributional meaning-in-use hypothesis, deriving semantic representations from word co-occurrence statistics. However, current deep learning methods entail fundamentally new models of lexical…
LLMs as models for analogical reasoning
Analogical reasoning — the capacity to identify and map structural relationships between different domains — is fundamental to human cognition and learning. Recent studies have shown that large language models (LLMs) can sometimes match humans in analogical reasoning tasks, opening the possibility that analogical reasoning might emerge from domain-general processes. However, it is still debated whether these emergent capacities are largely superf…
LLMs model how humans induce logically structured rules
Natural Language Processing Techniques (2 works) · Psychology (2 works) · Analogical reasoning (1 works) · Analogy (1 works) · Artificial Intelligence (1 works) · Cognition (1 works) · Cognitive psychology (1 works) · Computational semantics (1 works) · Computer Science (1 works) · Deep learning (1 works)