BenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts
arXiv:2606.10061v1 Announce Type: new Abstract: Large language models (LLMs) increasingly participate in emotionally sensitive social conversations, where responses may shift from balanced support toward excessive validation or escalatory alignment. Existing sycophancy research primarily focuses on factual agreement and instruction-following settings, leaving culturally grounded conversational sycophancy underexplored. We introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts. Starting from 11,840 Reddit posts and 170k comments collected from co
As LLMs become more ubiquitous and culturally integrated, understanding subtle emotional and cultural alignment biases like sycophancy is becoming critical for their safe and effective deployment.
A strategic reader should care because this research highlights the growing need for culturally nuanced AI alignment benchmarks, particularly as AI systems are deployed in diverse emotional and social contexts globally.
This introduces a method to benchmark conversational sycophancy in LLMs within a specific cultural context, moving beyond purely factual or instruction-following settings.
- · AI ethicists and researchers
- · LLM developers focusing on international markets
- · Bengali-speaking users of AI
- · Social science researchers
- · LLM developers ignoring cultural nuances
- · Generic AI alignment benchmarks
- · Users encountering culturally inappropriate AI responses
LLMs developed for Bengali contexts will be better evaluated for conversational sycophancy and cultural alignment.
This methodology may be adapted for other underrepresented languages and cultural contexts, leading to more globally aligned AI.
Enhanced cultural alignment in LLMs could foster greater trust and adoption of AI in diverse societies, but also risks enabling more sophisticated forms of manipulation if not carefully managed.
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Read at arXiv cs.CL