Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services

TL;DR

Pet-Bench is a comprehensive benchmark designed to evaluate large language models' abilities in virtual pet companionship, focusing on self-evolution, interaction, and emotional engagement to advance human-pet interaction technology.

Contribution

Introduces Pet-Bench, a novel benchmark for assessing LLMs in virtual pet roles, emphasizing developmental behaviors and diverse interactive tasks.

Findings

Significant performance variation among 28 LLMs based on size and capabilities.

Pet-Bench provides a realistic assessment of LLMs' pet-like behaviors.

Benchmark facilitates future optimization for emotionally immersive human-pet interactions.

Abstract

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.