title: Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
summary: 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.
paper_authors._id: 6835ba5b5ff50d406844b4cd
paper_authors.name: Nour Jedidi
paper_authors._id: 6835ba5b5ff50d406844b4ce
paper_authors.name: Yung-Sung Chuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:50:59.341Z
paper_authors._id: 5df84571da6d0311fd3d5407
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1650651305661-5df84571da6d0311fd3d5407.png
paper_authors.fullname: Yung-Sung Chuang
paper_authors.type: user
paper_authors.user: voidism
paper_authors._id: 6835ba5b5ff50d406844b4cf
paper_authors.name: James Glass
paper_authors._id: 6835ba5b5ff50d406844b4d0
paper_authors.name: Jimmy Lin
paper_title: Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Large Language Models pointwise rerankers reasoning process