Papers by Raja Marjieh
MacGyver: Are Large Language Models Creative Problem Solvers? (2024.naacl-long)
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Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas Griffiths, Faeze Brahman
| Challenge: | a new study examines the creative problem-solving capabilities of modern LLMs . it provides insight into the constrained problem- solving capabilities of both humans and AI . |
| Approach: | They use an automatically generated dataset to compare and contrast LLMs and humans to find out their creative problem-solving abilities. |
| Outcome: | The proposed dataset compares LLMs and humans in a constrained setting . it shows that humans excel in tasks they are familiar with but struggle with domain-specific knowledge . |
Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People (2024.acl-long)
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| Challenge: | Existing taxonomies or text corpora suffer from experimenter bias and are not representative of real-world distributions. |
| Approach: | They propose an iterative method for simultaneously eliciting conversational tones and sentences . they run 50 iterations with human participants and GPT-4 and obtain a dataset of sentences and frequent conversational tone. |
| Outcome: | The proposed method can be used to characterize the differences between humans and LLMs. |