Papers with factorial design
LVLMs and Humans Ground Differently in Referential Communication (2026.acl-long)
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Peter Zeng, Weiling Li, Amie J. Paige, Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras, Gregory J. Zelinsky, Susan Brennan, Owen Rambow
| Challenge: | generative AI agents cannot model common ground in a way that enables smooth communication . a recent study examined whether large language models and large vision language models engage in grounding as human discourse partners do . |
| Approach: | They propose to use referential communication to model common ground between a pair of directors and a picture matching system. |
| Outcome: | The proposed experiment shows that generative AI agents cannot model common ground . human conversation relies on common ground accrued and updated by interacting partners . |
Model-Dependent Moderation: Inconsistencies in Hate Speech Detection Across LLM-based Systems (2025.findings-acl)
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| Challenge: | Content moderation systems powered by large language models are increasingly deployed to detect hate speech . if two systems produce different outcomes for the same content, it undermines consistency and predictability . |
| Approach: | They analyze 1.3+ million sentences from a factorial design to determine hate speech classification . they find identical content receives markedly different classification values across systems . |
| Outcome: | The proposed model finds that identical content receives markedly different classification values across systems. |