Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

Joohyun Lee; Minji Roh

REVIEW 4 major objections 5 minor 14 references

Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A finance RAG pipeline with a multi-reranker cascade and a 32k-token split rule placed second in the FinanceRAG challenge.

desk verdict A workmanlike competition report with a useful ablation but an unevaluated and under-specified long-context splitting rule; the 2nd-place claim lacks generation metrics and final leaderboard details. read the letter →

arxiv 2411.16732 v1 pith:UO3H4RSE submitted 2024-11-23 cs.CL cs.IR

Joohyun Lee , Minji Roh This is my paper · ORCID

classification cs.CLcs.IR

keywords Retrieval-AugmentedGenerationFinanceRAGRerankerQueryExpansionLong-contextmanagementFinancialdocumentanalysisNDCGNumericalreasoning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a competition report describing a retrieval-augmented generation system built for the FinanceRAG challenge, and its central claim is that a particular pipeline configuration achieves competitive performance on financial question answering over 10-K reports, earnings reports, and other corporate documents. The system expands the query with extracted keywords, retrieves candidates with a lightweight cross-encoder reranker, then re-ranks with a stronger, dataset-specialized reranker, and finally feeds up to twenty retrieved contexts to an LLM. When the combined input exceeds 32,000 tokens, the system splits the contexts in half, generates an answer for each half, and fuses the two responses. The authors report that this pipeline placed second in the competition with a Task 1 public-leaderboard NDCG@10 score of 0.63996, and they support the design choices with ablation measurements showing that keyword extraction beats paraphrase and hypothetical-document expansion, and that table extraction helps on the MultiHiertt dataset.

What carries the argument

The central mechanism is the pipeline in the paper's Algorithm 1. Stage one expands the query by appending extracted keywords and, for the MultiHiertt dataset only, replaces large table-heavy corpora with extracted tables. Stage two performs retrieval through two reranker models in sequence: a fast multilingual cross-encoder (jina-reranker-v2-base-multilingual) selects the top 200 candidates, and a stronger reranker (chosen per dataset among gte-multilingual-reranker-base, bge-reranker-v2-m3, or the same jina model) selects the top 20. A reranker is a model that reads a query and a passage together and outputs a relevance score, in contrast to embedding similarity, which compares them separately. Stage three is the generation rule: if the token count of query plus top-20 contexts is at most 32k, the LLM sees them in one call; otherwise the contexts are split into two halves, each half is processed in a separate call, and the two answers are fused. This split-and-fuse rule is the paper's proposed method for managing long contexts without summarization.

What would settle it

Run the generation stage of the pipeline separately for each of the seven datasets, recording answer accuracy for queries whose supporting evidence falls on opposite sides of the 32k split boundary; if answers to such straddling queries are systematically worse than for queries contained in one half, the split-and-fuse rule is to blame, and varying the threshold from 16k to 64k should change which queries are hurt.

Watch

Extended reading notes

Core claim

The paper claims that its three-stage pipeline — keyword-based query expansion, a two-pass reranking cascade, and a long-context split-and-fuse rule — is what makes the system competitive. In the first pass, jina-reranker-v2-base-multilingual selects the top 200 corpus entries; in the second pass, a stronger reranker chosen per dataset (gte-multilingual-reranker-base, bge-reranker-v2-m3, or the same jina model) selects the top 20. If the token count of the query plus those twenty contexts is at most 32k, the LLM processes them in one call; otherwise the contexts are split into two halves, each half is processed separately, and the answers are fused. The ablation study in Table 1 shows the best pre-retrieval configuration combines the original query with extracted keywords rather than paraphrases or hypothetical documents, and applies table extraction only to the MultiHiertt corpus. The authors state that using pre-summarized corpora instead of the split-and-fuse rule significantly degraded responses for queries requiring specific numerical values, which is why the split-and-fuse approach was retained.

Load-bearing premise

The load-bearing premise is that a single 32,000-token context window is the right cutoff for every query in all seven FinanceRAG datasets; if numerical and narrative questions degrade at different context lengths, splitting the evidence in half could separate a query from the corpus needed to answer it, and the paper offers no per-dataset generation evaluation to rule that out.

Editorial extensions

If this is right

If the reported results hold, a reranker-only retrieval cascade, with no embedding-similarity step, is enough to reach competitive retrieval quality on finance RAG tasks.
The ablation results indicate that keyword extraction is the most effective query-expansion strategy among those tested for this corpus, outperforming paraphrasing and hypothetical-document generation.
The split-and-fuse rule for contexts above 32k tokens is a practical alternative to summarization for long financial documents, preserving numerical accuracy better than pre-summarized inputs.
Applying table extraction only to the MultiHiertt dataset suggests that the benefit of table preprocessing depends on the document structure of each dataset, not on all financial corpora.
The dataset-specific reranker assignments imply that no single reranker dominates all financial retrieval tasks, and choosing the reranker per dataset is part of the performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 32k threshold is a single global hyperparameter; one could test whether per-dataset thresholds, or splitting at evidence boundaries rather than the midpoint, would improve accuracy for queries whose evidence spans the split.
The paper selects rerankers using competition-provided labels, so the dataset-specific assignments in Table 2 may overfit to the public test set; evaluating the pipeline with cross-validation or on held-out FinanceRAG-style data would test how well the choices generalize.
The divide-and-conquer generation strategy could transfer to other document-heavy verticals, such as legal or medical question answering, but the 32k threshold and the fusion method would need to be tuned for those domains.
The paper does not report per-query cost; since the system pays for LLM calls for expansion, generation, and fusion, a cost-quality comparison against a single-call long-context model would be a natural next step.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a short competition report, not a research advance. It does a decent job of describing a sensible RAG pipeline for the FinanceRAG challenge, and the ablation in Table 1 is the most useful part. But the paper's most distinctive claim—that the long-context splitting method 'significantly' improves generation quality—has no generation metric behind it, and the way Algorithm 1 splits by rank rather than token count is a real gap between the description and the stated threshold.

What's actually new: essentially nothing conceptual. Query expansion, reranker ensembles, and context truncation are all cited (refs 6, 8, 10, 7) and used as engineering choices. The only concrete contributions are the dataset-to-reranker mapping in Table 2 and the 32k split-and-fuse rule. The ablation is genuinely informative: it shows keyword extraction helps and hypothetical documents hurt on this corpus, and that table extraction is worth it for MultiHiertt. That's a solid piece of empirical reporting for a 4-page paper.

The soft spots are mostly in the generation phase. The paper reports only Task 1 NDCG@10 (0.63996 on the public leaderboard). Task 2 results are absent, so the claim of improved response quality is unsupported. Also, as the stress-test I saw points out, Algorithm 1 splits the top-20 list in half by rank, not by token count. If one retrieved document exceeds 32k tokens—which the paper itself says happens in MultiHiertt—both halves can still be over the limit. The paper should either describe a token-aware packing strategy or explicitly bound the single-document size. And because fusion of two separate LLM passes can't do joint reasoning across evidence split between halves, the paper needs a comparison against simpler baselines (top-10 only, token-budget packing) to justify the split.

The leaderboard claim is also under-specified: it says '2nd place' but only gives the public LB score, not the final ranking. That's not necessarily wrong, but it makes the headline claim un-auditable. Code is promised but I couldn't verify it from the manuscript.

Bottom line: this is a workmanlike system description that would be fine for a workshop or short-paper track after revision. It's not a method paper, and I wouldn't cite it as introducing a new technique. But the ablation and the candid account of label-based model selection have some value for people building finance RAG systems. I'd send it to a serious referee, mainly to force the authors to add Task 2 numbers and clarify the splitting rule.

Referee Report

4 major / 5 minor

Summary. The paper describes the authors' RAG system for the ACM-ICAIF '24 FinanceRAG challenge. The pipeline consists of query expansion (original query plus keyword extraction), corpus table extraction for MultiHiertt data, a two-stage reranking procedure using three reranker models selected per dataset, and a generation phase that attempts to manage long contexts by splitting the top-20 retrieved corpora into two halves (top 1-10 and top 11-20) whenever the combined token count exceeds 32k, then fusing the two LLM answers. The paper reports an NDCG@10 of 0.63996 on the public leaderboard for Task 1 and claims 2nd place in the challenge. Contributions are stated as a pre-retrieval ablation study, an enhanced retrieval algorithm, and a new long-context management method.

Significance. If the claims are correct, this is a useful engineering contribution to finance-specific RAG, especially for numerical and tabular question answering over long documents. The ablation in Table 1 is transparent and provides some evidence for the choice of keyword extraction plus table extraction. The paper also ships source code, which supports reproducibility. However, the significance is limited by the absence of any generation-quality metric, any Task 2 result, or any final leaderboard placement beyond a single public NDCG@10 value. The central '2nd place' claim and the claimed benefit of the long-context method cannot be audited from the manuscript as written.

major comments (4)

[Section 3.3 and Algorithm 1] The long-context splitting and fusion method is presented as the paper's novel contribution and is claimed to 'significantly improve response quality' (Abstract and Section 3.3), but no generation-quality metric is reported anywhere. The only quantitative result is Task 1 NDCG@10, which measures retrieval relevance, not answer quality. There are no Task 2 scores, no final leaderboard result, no human evaluation, and no comparison against alternatives such as using top-10 only or packing by token budget. Without this evidence, the central claims cannot be assessed.
[Algorithm 1 and Section 3.1] Algorithm 1 splits the retrieved list by rank (top 1-10 vs. top 11-20), not by token count. The condition in line 7 checks whether the total token count of Q' plus the top-20 corpora is at most 32k; if it is not, the method proceeds to process the two halves, each of which may independently exceed 32k. The paper itself notes that MultiHiertt corpora have 'extremely large token counts' (Section 3.1). If a single retrieved document is larger than 32k tokens, both halves of a rank-based split will remain over budget, causing the LLM to either truncate evidence or operate beyond the identified degradation threshold. This is a concrete technical flaw that should be fixed (e.g., by token-based packing) or justified with data showing such cases do not arise.
[Section 3.3 and dataset descriptions in Section 2] Several datasets used in the challenge (FinQA, TATQA, ConvFinQA, MultiHiertt) are explicitly designed to require multi-step numerical reasoning over tabular and textual evidence. Splitting the retrieved corpora into two disjoint halves and fusing two independent LLM answers prevents joint reasoning over evidence that is distributed across both halves. The paper gives no comparison of the split-and-fuse strategy against top-10-only retrieval or token-budget packing, and it reports no generation accuracy for these datasets. Since the claimed improvement is specifically in response quality for such tasks, this is a load-bearing gap.
[Section 3.2 and Table 2] The per-dataset reranker assignment in Table 2 was selected using the competition labels, i.e., the same labels that define the reported score. The paper reports only the final public NDCG@10 (0.63996) and does not provide per-dataset NDCG@10 values, alternative reranker assignments, confidence intervals, or significance tests. Because the reranker is a free parameter tuned on the evaluation labels, the robustness of the single reported leaderboard value is unclear. At minimum, the per-dataset breakdown should be reported.

minor comments (5)

[Section 3.3 heading] The heading 'Geneartion' is a typo and should read 'Generation'.
[Copyright block and main text] The conference name appears as 'AMC-ICAIF' in the copyright block while the main text uses 'ACM-ICAIF'; please use the correct acronym consistently.
[Table 1] The 'Table only*' row layout is confusing: the column header is 'Original Summary Table only*' but the row has a single checkmark under 'Table only' with an asterisk footnote. Clarify which columns apply to this row.
[Section 5] The conclusion states the system leverages 'hybrid embedding similarity functions,' but Section 3.2 explicitly says rerankers were used instead of embedding similarity comparisons. This inconsistency should be corrected.
[Section 3.3 and references [5,7]] The claim of performance degradation beyond 32k tokens is based on a blog post and a preprint, and the manual analysis is not described. Providing concrete degradation numbers or a small experiment would strengthen this justification, and adding a peer-reviewed primary source would be appropriate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports an empirical competition system evaluated on an external leaderboard, with no self-cited formal result or definitional reduction.

full rationale

The paper is an empirical system description for the FinanceRAG competition, not a derivation from first principles. Its central claim—achieving 2nd place with a Task 1 public NDCG@10 of 0.63996—is an externally evaluated outcome, not a quantity defined by the authors' own equations. The reranker models are selected per dataset using organizer-provided labels (Section 3.2), and the final public leaderboard score is then reported; this is supervised model selection on competition data, not a fitted parameter renamed as a prediction. The long-context handling in Section 3.3 is an explicitly stated heuristic (Algorithm 1) with an observed 32k threshold, and no claim is made that it follows from a formal theorem. None of the seven circularity patterns applies: there are no self-citations carrying the argument, no uniqueness theorem imported from the authors' prior work, no ansatz smuggled in via citation, and no renaming of a known result as new organization. The paper's gaps—no Task 2 generation metrics, no per-dataset generation evaluation, and rank-based splitting that may not respect token budgets—are soundness and completeness concerns, not circularity. The system is self-contained against an external benchmark, so the appropriate circularity finding is none, with score 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on engineering constants and model-selection decisions rather than unproved math or new entities. The main ledger items are hand-set thresholds (32k, top 200, top 20) and component choices fitted to the competition labels, plus assumptions that reranker score scales and long-context degradation behave consistently across datasets.

free parameters (4)

context_split_threshold = 32k tokens
Hand-set after manual inspection of competition data; the paper gives no sensitivity analysis or per-dataset validation for this cutoff (Section 3.3, Algorithm 1).
per_dataset_reranker_assignment = jina-reranker-v2-base-multilingual, gte-multilingual-reranker-base, or bge-reranker-v2-m3 per Table 2
Selected by testing top MTEB rerankers on the organizer-provided labels, making the component choice tuned to the target metric (Section 3.2, Table 2).
candidate_and_context_counts = top 200 candidates; top 20 for generation
Fixed in Algorithm 1 with no ablation or alternative values reported.
query_expansion_combination = original query plus keyword extraction
Chosen from the Table 1 ablation as the best combination; the table covers only one pass and reports no statistical variability.

assumptions (4)

domain assumption Raw reranker scores from different families can be compared with the same top-200 cutoff.
Jina, GTE, and BGE produce different score distributions, yet the pipeline uses raw scores to form the candidate set without normalization (Section 3.2).
domain assumption Public leaderboard scores are representative of final competition placement.
The final leaderboard results or prize placement are not reported, so the paper assumes the public NDCG@10 0.63996 reflects the system's final quality (Section 3.2).
domain assumption LLM answer quality degrades uniformly beyond 32k tokens for all FinanceRAG datasets.
The cutoff is generalized from cited long-context studies and manual analysis, with no per-dataset generation experiments (Section 3.3).
domain assumption Query expansion and table extraction preserve all numerical information needed for answers.
The system relies on preprocessing to keep critical numbers intact, especially in MultiHiertt tables, but does not evaluate information loss (Section 3.1).

how reviews work

0 comments

Cite this review

Pith. "Pith review of Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge." pith.science (2026). https://pith.science/paper/UO3H4RSE

@misc{pith2026241116732,
  author       = {Pith},
  title        = {Pith review of: Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UO3H4RSE}},
  note         = {Machine review of arXiv:2411.16732}
}

read the original abstract

As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable and time-consuming, such as analyzing financial statements, disclosures, and related documents, are now being effectively tackled using LLMs. This paper details the development of a high-performance, finance-specific Retrieval-Augmented Generation (RAG) system for the ACM-ICAIF '24 FinanceRAG competition. We optimized performance through ablation studies on query expansion and corpus refinement during the pre-retrieval phase. To enhance retrieval accuracy, we employed multiple reranker models. Notably, we introduced an efficient method for managing long context sizes during the generation phase, significantly improving response quality without sacrificing performance. We ultimately achieve 2nd place in the FinanceRAG Challenge. Our key contributions include: (1) pre-retrieval ablation analysis, (2) an enhanced retrieval algorithm, and (3) a novel approach for long-context management. This work demonstrates the potential of LLMs in effectively processing and analyzing complex financial data to generate accurate and valuable insights. The source code and further details are available at https://github.com/cv-lee/FinanceRAG.

Figures

Figures reproduced from arXiv: 2411.16732 by the authors.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 8 canonical work pages

[1]

Jina AI. 2024. Jina Reranker v2: Base Multilingual. https://huggingface.co/jinaai/ jina-reranker-v2-base-multilingual. Accessed: November 8, 2024

work page 2024
[2]

BAAI. 2024. BGE Reranker v2 M3. https://huggingface.co/BAAI/bge-reranker- v2-m3. Accessed: November 8, 2024

work page 2024
[3]

Chanyeol Choi, Jy-Yong Sohn, Yongjae Lee, Subeen Pang, Jaeseon Ha, Hoyeon Ryoo, Yongjin Kim, Hojun Choi, and Jihoon Kwon. 2024. ACM-ICAIF ’24 Fi- nanceRAG Challenge. https://kaggle.com/competitions/icaif-24-finance-rag- challenge. Kaggle

work page 2024
[4]

Google Cloud. 2024. Long Context Capabilities of Vertex AI Generative AI . Google Cloud. https://cloud.google.com/vertex-ai/generative-ai/docs/long-context?hl= ko

work page 2024
[5]

Databricks. 2024. The Long Context RAG Capabilities of OpenAI o1 and Google Gem- ini. Databricks. https://www.databricks.com/blog/long-context-rag-capabilities- openai-o1-and-google-gemini

work page 2024
[6]

Yunfan Gao, Yun Xiong, Meng Wang, and Haofen Wang. 2024. Modular rag: Trans- forming rag systems into lego-like reconfigurable frameworks. arXiv preprint arXiv:2407.21059 (2024)

arXiv 2024
[7]

Bowen Jin, Jinsung Yoon, Jiawei Han, and Sercan O Arik. 2024. Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG.arXiv preprint arXiv:2410.05983 (2024)

arXiv 2024
[8]

Hamin Koo, Minseon Kim, and Sung Ju Hwang. 2024. Optimizing Query Genera- tion for Enhanced Document Retrieval in RAG. arXiv preprint arXiv:2407.12325 (2024)

arXiv 2024

Show all 14 references

[9]

Alibaba NLP. 2024. GTE Multilingual Reranker Base. https://huggingface.co/ Alibaba-NLP/gte-multilingual-reranker-base. Accessed: November 8, 2024

2024
[10]

Chaitanya Patel. 2024. Hypothetical Retrieval-Augmented Generation (Hypothet- ical RAG): Advancing AI for Enhanced Contextual Understanding and Creative Problem-Solving. Scientific Research Journal of Science, Engineering and Technol- ogy 2, 1 (2024), 1–4

2024
[11]

Spurthi Setty, Harsh Thakkar, Alyssa Lee, Eden Chung, and Natan Vidra. 2024. Improving retrieval for rag based question answering models on financial docu- ments. arXiv preprint arXiv:2404.07221 (2024)

2024 arXiv
[12]

Antonio Jimeno Yepes, Yao You, Jan Milczek, Sebastian Laverde, and Renyu Li
[13]

Boyu Zhang, Hongyang Yang, Tianyu Zhou, Muhammad Ali Babar, and Xiao- Yang Liu. 2023. Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the fourth ACM international conference on AI in finance . 349–356

2023
[2024]

arXiv preprint arXiv:2402.05131 (2024)

Financial report chunking for effective retrieval augmented generation. arXiv preprint arXiv:2402.05131 (2024)

2024 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Jina AI. 2024. Jina Reranker v2: Base Multilingual. https://huggingface.co/jinaai/ jina-reranker-v2-base-multilingual. Accessed: November 8, 2024

work page 2024

[2] [2]

BAAI. 2024. BGE Reranker v2 M3. https://huggingface.co/BAAI/bge-reranker- v2-m3. Accessed: November 8, 2024

work page 2024

[3] [3]

Chanyeol Choi, Jy-Yong Sohn, Yongjae Lee, Subeen Pang, Jaeseon Ha, Hoyeon Ryoo, Yongjin Kim, Hojun Choi, and Jihoon Kwon. 2024. ACM-ICAIF ’24 Fi- nanceRAG Challenge. https://kaggle.com/competitions/icaif-24-finance-rag- challenge. Kaggle

work page 2024

[4] [4]

Google Cloud. 2024. Long Context Capabilities of Vertex AI Generative AI . Google Cloud. https://cloud.google.com/vertex-ai/generative-ai/docs/long-context?hl= ko

work page 2024

[5] [5]

Databricks. 2024. The Long Context RAG Capabilities of OpenAI o1 and Google Gem- ini. Databricks. https://www.databricks.com/blog/long-context-rag-capabilities- openai-o1-and-google-gemini

work page 2024

[6] [6]

Yunfan Gao, Yun Xiong, Meng Wang, and Haofen Wang. 2024. Modular rag: Trans- forming rag systems into lego-like reconfigurable frameworks. arXiv preprint arXiv:2407.21059 (2024)

arXiv 2024

[7] [7]

Bowen Jin, Jinsung Yoon, Jiawei Han, and Sercan O Arik. 2024. Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG.arXiv preprint arXiv:2410.05983 (2024)

arXiv 2024

[8] [8]

Hamin Koo, Minseon Kim, and Sung Ju Hwang. 2024. Optimizing Query Genera- tion for Enhanced Document Retrieval in RAG. arXiv preprint arXiv:2407.12325 (2024)

arXiv 2024