Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
With the growing success of reasoning models across complex natural language
tasks, researchers in the Information Retrieval (IR) community have begun
exploring how similar reasoning capabilities can be integrated into passage
rerankers built on Large Language Models (LLMs). These methods typically employ
an LLM to produce an explicit, step-by-step reasoning process before arriving
at a final relevance prediction. But, does reasoning actually improve reranking
accuracy? In this paper, we dive deeper into this question, studying the impact
of the reasoning process by comparing reasoning-based pointwise rerankers
(ReasonRR) to standard, non-reasoning pointwise rerankers (StandardRR) under
identical training conditions, and observe that StandardRR generally
outperforms ReasonRR. Building on this observation, we then study the
importance of reasoning to ReasonRR by disabling its reasoning process
(ReasonRR-NoReason), and find that ReasonRR-NoReason is surprisingly more
effective than ReasonRR. Examining the cause of this result, our findings
reveal that reasoning-based rerankers are limited by the LLM's reasoning
process, which pushes it toward polarized relevance scores and thus fails to
consider the partial relevance of passages, a key factor for the accuracy of
pointwise rerankers.
6835ba5b5ff50d406844b4cd
Nour Jedidi
6835ba5b5ff50d406844b4ce
Yung-Sung Chuang
claimed_verified
2025-06-03T08:50:59.341Z
5df84571da6d0311fd3d5407
https://cdn-avatars.huggingface.co/v1/production/uploads/1650651305661-5df84571da6d0311fd3d5407.png
Yung-Sung Chuang
user
voidism
6835ba5b5ff50d406844b4cf
James Glass
6835ba5b5ff50d406844b4d0
Jimmy Lin
Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
With the growing success of reasoning models across complex natural language
tasks, researchers in the Information Retrieval (IR) community have begun
exploring how similar reasoning capabilities can be integrated into passage
rerankers built on Large Language Models (LLMs). These methods typically employ
an LLM to produce an explicit, step-by-step reasoning process before arriving
at a final relevance prediction. But, does reasoning actually improve reranking
accuracy? In this paper, we dive deeper into this question, studying the impact
of the reasoning process by comparing reasoning-based pointwise rerankers
(ReasonRR) to standard, non-reasoning pointwise rerankers (StandardRR) under
identical training conditions, and observe that StandardRR generally
outperforms ReasonRR. Building on this observation, we then study the
importance of reasoning to ReasonRR by disabling its reasoning process
(ReasonRR-NoReason), and find that ReasonRR-NoReason is surprisingly more
effective than ReasonRR. Examining the cause of this result, our findings
reveal that reasoning-based rerankers are limited by the LLM's reasoning
process, which pushes it toward polarized relevance scores and thus fails to
consider the partial relevance of passages, a key factor for the accuracy of
pointwise rerankers.
Reasoning-based rerankers using Large Language Models do not improve accuracy compared to standard rerankers and are outperformed even when their reasoning process is disabled due to overly polarized relevance scores.
Large Language Models pointwise rerankers reasoning process