search agent data synthesis details
First, I would like to express my sincere appreciation for your groundbreaking work. This work has significantly advanced the field, and I am eager to deepen my understanding of certain technical details.
I write to seek further clarification on the Search Agent data synthesis pipeline detailed in Section 3.2.3, as some steps remain ambiguous to me. Specifically, while you outline a multi-agent workflow (sampling long-tail entities → exploring entities → generating candidate answers → validating samples), the implementation details of Steps 2, 3, and the origin of “ground-truth vs. candidate answers” in Step 4 are not fully elaborated.
- For Step 2: You mention a “question-construction agent (QCA) explores each entity using search tools with configurable depth and breadth parameters, consolidating information into question-answer (QA) pairs.” Could you elaborate on how “depth” and “breadth” are concretely defined and configured (e.g., Is “depth” the number of iterative search rounds? Is “breadth” the number of expanded keywords per round?) and how the QCA transforms unstructured search results into structured QA pairs (e.g., Does it first generate a question about the entity, then search for answers, or vice versa)?
- For Step 3: You note “multiple answer-generation agents with heterogeneous configurations (different checkpoints, system prompts, etc.) produce diverse candidate responses for each proposed QA pair.” what's the difference between "answer" and "response"?
- Finally, for Step 4: You state the verification agent retains “samples where the ground-truth is correct and all candidates are verifiably incorrect.” However, the origin of the “ground-truth” here is unclear—Is it the QA pair output by the QCA in Step 2, or is it a separate, human-validated/authoritative answer collected independently? And are the “candidates” explicitly the outputs from Step 3’s answer-generation agents?
These details would greatly help in replicating your pipeline and understanding how the synthesized data ensures both diversity and factual reliability for training Search Agents.
I also invite other readers to share their interpretations or insights—collaborative discussion will no doubt further enrich our understanding of this impactful work.
Thank you again for your contributions to the community!