Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents
In the rapidly evolving realm of artificial intelligence, the effectiveness of data-science agents is increasingly scrutinized. As these agents transition from serving as co-pilots to fully autonomous systems, the challenge of silent misframing has emerged as a critical failure mode. This phenomenon occurs when agents commit to ambiguous task framings that, while appearing plausible, lead to unintended outcomes. The implications of this misframing can be severe, as agents may produce clean and executable artifacts that mask their incorrect interpretations of the task at hand.
To address this pressing issue, researchers have introduced a new benchmark known as Ambig-DS, designed specifically to evaluate task-framing ambiguity in data-science agents. Unlike existing benchmarks that typically measure whether a pipeline runs successfully, Ambig-DS focuses on whether the agent accurately recognizes the task’s specifications. This benchmark comprises two diagnostic suites aimed at assessing different dimensions of ambiguity:
- Ambig-DS-Target: This suite includes 51 tasks built on DSBench, a benchmark for tabular modeling.
- Ambig-DS-Objective: This suite features 61 tasks constructed from MLE-bench, a Kaggle-style machine learning competition benchmark.
Each suite is meticulously designed so that scoring leverages the original evaluators from their respective source benchmarks. For every task, researchers pair a fully specified version with an ambiguous variant that has been generated through controlled edits. A human-and-LLM verification pipeline is employed to confirm that each ambiguous variant admits multiple plausible interpretations, each with decision-relevant consequences.
Initial analyses of these suites reveal that ambiguity significantly lowers performance across various agents, ranging from efficient to frontier-class models. The findings from these controlled diagnostic settings are both revealing and concerning:
- Silent Commitments: Failures manifest as silent commitments, such as incorrect target submissions on Ambig-DS-Target or inappropriate baseline submissions on Ambig-DS-Objective, rather than execution errors.
- Clarifying Questions: Allowing agents to pose one clarifying question can substantially recover performance losses under ideal conditions, indicating that missing framing information contributes significantly to observed performance degradation.
- Inconsistent Use of Questions: Despite the potential benefits, agents often struggle to determine when to utilize this capability. Permissive prompts can lead to over-asking on clear tasks, while conservative prompts may encourage silent defaulting on ambiguous tasks.
The foundational insight derived from this research is that recognizing target and objective underspecification is a critical bottleneck that is often overlooked in standard evaluations of data-science agents. As the capabilities of these agents continue to advance, addressing the nuances of task framing will be essential to harnessing their full potential.
In conclusion, Ambig-DS not only presents a systematic approach to understanding the implications of task-framing ambiguity but also underscores the necessity for enhanced evaluation metrics in the development of autonomous data-science agents. As AI continues to integrate into various sectors, ensuring the reliability and accuracy of these systems remains paramount.
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