Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914201098780672 |
|---|---|
| author | Guo, Hongcheng Xie, Zheyong Cao, Shaosheng Wang, Boyang Liu, Weiting Ye, Zheyu Li, Zhoujun Liu, Zuozhu Lu, Wei |
| author_facet | Guo, Hongcheng Xie, Zheyong Cao, Shaosheng Wang, Boyang Liu, Weiting Ye, Zheyu Li, Zhoujun Liu, Zuozhu Lu, Wei |
| contents | As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Existing approaches focus on basic pet role-playing interactions without systematically benchmarking LLMs for comprehensive companionship. In this paper, we introduce Pet-Bench, a dedicated benchmark that evaluates LLMs across both self-interaction and human-interaction dimensions. Unlike prior work, Pet-Bench emphasizes self-evolution and developmental behaviors alongside interactive engagement, offering a more realistic reflection of pet companionship. It features diverse tasks such as intelligent scheduling, memory-based dialogues, and psychological conversations, with over 7,500 interaction instances designed to simulate pet behaviors. Evaluation of 28 LLMs reveals significant performance variations linked to model size and inherent capabilities, underscoring the need for specialized optimization in this domain. Pet-Bench serves as a foundational resource for benchmarking pet-related LLM abilities and advancing emotionally immersive human-pet interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services Guo, Hongcheng Xie, Zheyong Cao, Shaosheng Wang, Boyang Liu, Weiting Ye, Zheyu Li, Zhoujun Liu, Zuozhu Lu, Wei Computation and Language As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Existing approaches focus on basic pet role-playing interactions without systematically benchmarking LLMs for comprehensive companionship. In this paper, we introduce Pet-Bench, a dedicated benchmark that evaluates LLMs across both self-interaction and human-interaction dimensions. Unlike prior work, Pet-Bench emphasizes self-evolution and developmental behaviors alongside interactive engagement, offering a more realistic reflection of pet companionship. It features diverse tasks such as intelligent scheduling, memory-based dialogues, and psychological conversations, with over 7,500 interaction instances designed to simulate pet behaviors. Evaluation of 28 LLMs reveals significant performance variations linked to model size and inherent capabilities, underscoring the need for specialized optimization in this domain. Pet-Bench serves as a foundational resource for benchmarking pet-related LLM abilities and advancing emotionally immersive human-pet interactions. |
| title | Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.03761 |