9 accepted papers
Text-to-image (T2I) systems increasingly rely on upstream prompters, either humans or multimodal large language models (MLLMs), to translate user intent into detailed prompts. Yet current benchmarks fix the prompt and only evaluate T2I models, leaving the prompting proficiency of this upstream compo…
Large language models (LLMs) increasingly shape interpersonal and societal decision-making, yet their ability to navigate explicit conflicts between legitimate cultural values remains underexplored. Existing benchmarks focus on cultural knowledge (CulturalBench), value inference (WorldValuesBench),
LLM-powered coding agents are reshaping the development paradigm. However, existing evaluation systems, neither traditional tests for humans nor benchmarks for LLMs, fail to capture this shift, excluding problems that require both human reasoning to guide solutions and AI efficiency for implementati…
Collectively exploring and understanding an environment is an open challenge, particularly in dynamic settings where agents must rely on limited information that may only be intermittently available. In this paper, we focus on how agents can maximize information capture in these contexts. As agents …
Social robots have demonstrated great potential in various domains. Recent advancements in Large Language Models (LLMs) have expanded the conversational capabilities of these robots, enabling more personalized user interactions. However, current systems primarily focus on behavior or task personaliz…
Despite the rapid advancement of LLM-based agents, the reliable evaluation of their safety and security remains a significant challenge. Existing rule-based or LLM-based evaluators often miss dangers in agents' step-by-step actions, overlook subtle meanings, fail to see how small issues compound, an…
The capacity to attribute mental states like beliefs, desires, and intentions to oneself and others, known as Theory of Mind (ToM), is fundamental to human social intelligence. As Large Language Models (LLMs) are increasingly integrated into complex interactive systems, developing their ToM capabili
Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source L…
The advancements of Large Language Models (LLMs) have spurred a growing interest in their application to Named Entity Recognition (NER) methods. However, existing datasets are primarily designed for traditional machine learning methods and are inadequate for LLM-based methods, in terms of corpus sel