Our AI Reviewer Got the Verdict Right and the Explanation Wrong
We are building a local agent that can write short descriptions and topics from source material, then check its own work. The goal is not just to get an answer out. We want it to finish the task without inventing facts, manufacturing problems or damaging something it already got right. That last part has been harder than it sounds. During our own tests, the reviewer sometimes reached the right…
Our team has developed an AI agent capable of summarizing source material and reviewing its own work. This review process aims to ensure accuracy by identifying issues such as invented facts, fabricated problems, and the misrepresentation of correct information. However, the reviewer sometimes reaches the right decision while providing an incorrect reason, or rejects a useful answer while missing an error in the source text.
In some cases, the reviewer correctly identifies unsupported results but incorrectly accuses the description of plagiarism. These errors can have significant consequences, as a false accusation might encourage writers to paraphrase instead of correcting the actual issue. The review also sometimes erroneously identifies missing details as factual failures, which contradicts the goal of producing concise summaries.
Additionally, the reviewer failed to catch certain copying defects and structural errors, even when they were present in the candidate outputs. When we added additional procedural guidance to the reviewer's instructions, the number of fully qualifying reviews decreased from six to three out of 12 inputs. This finding suggests that increasing the critic's suspicion is not a reliable substitute for accurately distinguishing between correct and incorrect decisions.
Our evaluation involved testing under two different prompt conditions, and the results under each condition remained consistent, indicating that the change did not improve the agent's performance. Throughout the development process, we performed hands-on human reviews and had other model families independently regrade the same candidates and critiques.
The consistent results across different evaluations reinforce the importance of ensuring that the reviewer's decisions are both correct and well-explained. Ultimately, our goal is to verify whether the writer's explanation would effectively address the actual defect, as a correct verdict alone is not sufficient if the explanation is not truthful.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
