CalVerT：通过使用经过校准的验证器遥测数据来增强智能体，能够提升对知识密集型任务中的操作能力与学习效果
CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks
摘要
在知识密集型问答场景中，LLM智能体需要利用检索和推理机制来处理那些信息不完整的状况——即当前答案是否不确定、缺乏依据，或者已经完整。这导致了两种失败模式：要么给出看似可靠但缺乏依据的答案，从而降低准确性；要么在已有足够证据的情况下仍然进行不必要的检索，导致计算资源被浪费。为了让智能体能够更全面地了解所处状态，我们引入了经过校准的验证器遥测数据（CalVerT），该数据会为智能体的状态信息增加额外的信息：包括经过校准的自信度评分以及基于实际情境的验证评分。我们发现，CalVerT可以在无需训练或基于训练的情况下提升智能体的性能。在四个QA测试集上，我们发现当智能体过度依赖参数化知识时，CalVerT能促使它们进行正确的检索操作，从而提升F1分数；而当智能体拥有足够的上下文信息来给出答案时，则能避免不必要的检索，减少资源浪费。此外，CalVerT还可以在不进行训练的情况下增强现有的QA系统。而且，对于已经经过训练的系统来说，通过添加这些遥测数据到智能体的状态中，也能带来改进效果——与那些接受相同训练但没有CalVerT数据的智能体相比，这种改进更为明显。
English Abstract
LLM agents in knowledge intensive question answering take retrieval and reasoning actions with incomplete knowledge about whether their current answer is uncertain, unsupported, or already complete. This produces two failure modes: committing to confident but unsupported answers, which hurts accuracy, and over-retrieving when the evidence in hand already suffices, resulting in wasted compute. To give agents a more complete picture of the state space they are operating in, we introduce calibrated verifier telemetry (CalVerT), which augments the agent's state with additional telemetry: a calibrated self-confidence score and a grounding verifier score. We show that CalVerT can improve agents in both training-free and training-based settings. On four QA benchmarks, we find that CalVerT raises F1 by triggering retrieval in cases where agents over-rely on parametric knowledge, while cutting redundant retrieval in cases where agents have sufficient context to answer. We show that CalVerT can augment existing QA frameworks without training. Moreover, CalVerT also improves trained systems: by simply augmenting an agent's state with telemetry, we observe improvements after reinforcement learning, as compared to an agent with identical training but no CalVerT telemetry.