H2HTalk：评估作为情感伴侣的大型语言模型
H2HTalk: Evaluating Large Language Models as Emotional Companion
AI总结：
本文提出H2HTalk基准，通过4,650个场景和SAP安全依恋模块系统评估50个LLM作为情感伴侣的共情互动与个性发展能力，发现长时程规划与记忆保持仍是主要挑战。
摘要：
随着数字情感支持需求的增长，大型语言模型伴侣提供了有前景的真实、随时可用的共情能力，但严格的评估仍落后于模型的进步。我们提出了Heart-to-Heart Talk（H2HTalk），一个用于评估伴侣在个性发展和共情互动方面表现的基准，兼顾情商与语言流畅性。H2HTalk包含4,650个精心策划的场景，涵盖对话、回忆和行程规划，模拟真实世界中的支持性对话，在规模和多样性上显著超越了以往的数据集。我们引入了一个安全依恋人格（Secure Attachment Persona，SAP）模块，该模块应用依恋理论原则以实现更安全的互动。使用我们的统一协议对50个LLM进行基准测试后发现，长时程规划和记忆保持仍然是关键挑战，当用户需求隐含或在对话中途发生变化时，模型表现困难。H2HTalk建立了首个面向情感智能伴侣的综合基准。我们发布所有材料，以推动能够提供有意义且安全的心理支持的LLM的发展。
英文摘要：
As digital emotional support needs grow, Large Language Model companions offer promising authentic, always-available empathy, though rigorous evaluation lags behind model advancement. We present Heart-to-Heart Talk (H2HTalk), a benchmark assessing companions across personality development and empathetic interaction, balancing emotional intelligence with linguistic fluency. H2HTalk features 4,650 curated scenarios spanning dialogue, recollection, and itinerary planning that mirror real-world support conversations, substantially exceeding previous datasets in scale and diversity. We incorporate a Secure Attachment Persona (SAP) module implementing attachment-theory principles for safer interactions. Benchmarking 50 LLMs with our unified protocol reveals that long-horizon planning and memory retention remain key challenges, with models struggling when user needs are implicit or evolve mid-conversation. H2HTalk establishes the first comprehensive benchmark for emotionally intelligent companions. We release all materials to advance development of LLMs capable of providing meaningful and safe psychological support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
