COINBench: Moving Beyond Individual Perspectives to Collective Intent Understanding
Understanding human intent is a high-level cognitive challenge for Large Language Models (LLMs), requiring sophisticated reasoning over noisy, conflicting, and non-linear discourse. While LLMs excel at following individual instructions, their ability to distill Collective Intent - the process of extracting consensus, resolving contradictions, and inferring latent trends from multi-source public discussions - remains largely unexplored. To bridge this gap, we introduce COIN-BENCH, a dynamic, real-world, live-updating benchmark specifically designed to evaluate LLMs on collective intent understanding within the consumer domain. Unlike traditional benchmarks that focus on transactional outco...
| Authors | Xiaozhe Li, Tianyi Lyu, Siyi Yang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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