ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models
This addresses the problem of enabling MLLMs to proactively seek help for better collaboration, which is incremental as it builds on existing benchmarks and methods.
The paper introduced ProactiveBench, a benchmark to test proactiveness in multimodal large language models (MLLMs) by evaluating their ability to request user interventions in tasks like recognizing occluded objects, showing that MLLMs generally lack proactiveness and that fine-tuning can improve it.
Effective collaboration begins with knowing when to ask for help. For example, when trying to identify an occluded object, a human would ask someone to remove the obstruction. Can MLLMs exhibit a similar "proactive" behavior by requesting simple user interventions? To investigate this, we introduce ProactiveBench, a benchmark built from seven repurposed datasets that tests proactiveness across different tasks such as recognizing occluded objects, enhancing image quality, and interpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench, showing that (i) they generally lack proactiveness; (ii) proactiveness does not correlate with model capacity; (iii) "hinting" at proactiveness yields only marginal gains. Surprisingly, we found that conversation histories and in-context learning introduce negative biases, hindering performance. Finally, we explore a simple fine-tuning strategy based on reinforcement learning: its results suggest that proactiveness can be learned, even generalizing to unseen scenarios. We publicly release ProactiveBench as a first step toward building proactive multimodal models.