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arxiv: 2507.07847 · v3 · submitted 2025-07-10 · 💻 cs.CL · cs.AI

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems

Youngjoon Jang , Seongtae Hong , Junyoung Son , Sungjin Park , Chanjun Park , Heuiseok Lim This is my paper

Pith reviewed 2026-05-19 05:27 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords coreference resolutionretrieval-augmented generationquestion answeringnatural language processingentity disambiguationlarge language modelsretrieval effectiveness
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The pith

Coreference resolution improves retrieval effectiveness and question-answering performance in RAG systems.

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

The paper tests whether resolving references such as pronouns in retrieved documents reduces ambiguity that interferes with how large language models use context in retrieval-augmented generation. It reports gains in both how well documents are retrieved and how accurately questions are answered once coreferences are fixed. A sympathetic reader would care because clearer references could let RAG systems deliver more consistent facts with less hallucination, especially when using smaller models that have less built-in ability to track entities.

Core claim

We demonstrate that coreference resolution enhances retrieval effectiveness and improves question-answering (QA) performance. Through comparative analysis of different pooling strategies in retrieval tasks, we find that mean pooling demonstrates superior context capturing ability after applying coreference resolution. In QA tasks, we discover that smaller models benefit more from the disambiguation process, likely due to their limited inherent capacity for handling referential ambiguity.

What carries the argument

Coreference resolution applied as preprocessing to reduce referential ambiguity in retrieved documents, enabling clearer in-context learning for the generation step.

If this is right

  • Mean pooling yields better retrieval results than other strategies once coreferences are resolved.
  • Smaller language models receive larger QA accuracy gains from the disambiguation than larger models.
  • Overall RAG response quality rises because referential ambiguity no longer interferes with factual grounding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standard RAG pipelines might adopt coreference resolution as routine preprocessing for any knowledge-intensive task.
  • The same disambiguation step could reduce errors in related settings that also rely on long retrieved contexts.
  • Testing across varied document domains would show whether the reported gains remain stable outside the evaluated setting.

Load-bearing premise

Coreferential complexity in retrieved documents is the main source of ambiguity that disrupts performance, and resolving it yields gains independent of document length or domain.

What would settle it

Compare retrieval precision and QA accuracy on the same document set before and after removing the coreference resolution step; a clear drop without it would support the claim.

Figures

Figures reproduced from arXiv: 2507.07847 by Chanjun Park, Heuiseok Lim, Junyoung Son, Seongtae Hong, Sungjin Park, Youngjoon Jang.

Figure 1
Figure 1. Figure 1: Example of changes in similarity and re [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
read the original abstract

Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, introducing ambiguity that disrupts in-context learning. In this study, we systematically investigate how entity coreference affects both document retrieval and generative performance in RAG-based systems, focusing on retrieval relevance, contextual understanding, and overall response quality. We demonstrate that coreference resolution enhances retrieval effectiveness and improves question-answering (QA) performance. Through comparative analysis of different pooling strategies in retrieval tasks, we find that mean pooling demonstrates superior context capturing ability after applying coreference resolution. In QA tasks, we discover that smaller models benefit more from the disambiguation process, likely due to their limited inherent capacity for handling referential ambiguity. With these findings, this study aims to provide a deeper understanding of the challenges posed by coreferential complexity in RAG, providing guidance for improving retrieval and generation in knowledge-intensive AI applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript examines the role of coreference resolution as a preprocessing step in Retrieval-Augmented Generation (RAG) pipelines. It claims that resolving referential ambiguity in retrieved documents improves retrieval effectiveness (particularly under mean pooling) and downstream QA performance, with smaller LLMs showing larger gains from the disambiguation process.

Significance. If the central empirical claims hold after addressing potential confounds, the work would offer a concrete, low-cost intervention for reducing ambiguity in RAG contexts and would supply practical guidance on when coreference resolution is most beneficial (e.g., for smaller models). The absence of machine-checked proofs or parameter-free derivations is expected for an empirical study; the value would lie in reproducible experimental comparisons.

major comments (1)
  1. [Experimental setup and results (comparative analysis of retrieval and QA tasks)] The central claim that observed gains are attributable to removal of referential ambiguity rather than incidental side-effects of the preprocessing step is not yet supported. Coreference resolution typically shortens or restructures passages; without explicit controls that hold document length, token count, or lexical density constant across the with/without-resolution conditions, any reported lift in retrieval or QA metrics could be explained by reduced context length or altered embedding statistics instead of the intended mechanism. This issue directly undermines the attribution in the abstract and the weakest assumption identified in the reader's report.
minor comments (1)
  1. [Abstract] The abstract asserts clear improvements yet supplies no datasets, metrics, statistical tests, baseline comparisons, or error analysis; these details must be added to the main text with sufficient precision for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The concern about potential confounds is well-taken, and we address it directly below by outlining planned revisions that will strengthen the attribution of observed gains to coreference resolution.

read point-by-point responses

  1. Referee: [Experimental setup and results (comparative analysis of retrieval and QA tasks)] The central claim that observed gains are attributable to removal of referential ambiguity rather than incidental side-effects of the preprocessing step is not yet supported. Coreference resolution typically shortens or restructures passages; without explicit controls that hold document length, token count, or lexical density constant across the with/without-resolution conditions, any reported lift in retrieval or QA metrics could be explained by reduced context length or altered embedding statistics instead of the intended mechanism. This issue directly undermines the attribution in the abstract and the weakest assumption identified in the reader's report.

    Authors: We agree that this is a substantive concern and that the current experiments do not fully isolate referential disambiguation from incidental effects of passage restructuring or length change. Coreference resolution can indeed alter token counts and lexical properties as a byproduct. To address this, we will add controlled experiments in the revised manuscript: (1) length-matched conditions in which resolved passages are truncated or padded to match the original token length before embedding; (2) explicit reporting of average token counts, sentence lengths, and lexical density for both resolved and unresolved conditions; and (3) an auxiliary baseline that applies artificial shortening without coreference resolution to test whether length reduction alone accounts for the gains. These additions will allow readers to evaluate whether the improvements persist when length and density are held constant, thereby strengthening the causal link to ambiguity removal. We view this as a necessary and feasible revision. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparison with no derivations or self-referential reductions

full rationale

The paper conducts an empirical investigation of coreference resolution's impact on RAG retrieval and QA performance via direct before/after comparisons and pooling strategy analysis. No equations, derivations, fitted parameters, or self-citations are invoked as load-bearing premises for the central claims; results derive from measured metrics on external benchmarks rather than reducing to inputs by construction. The work is self-contained against independent experimental controls and does not rely on uniqueness theorems, ansatzes smuggled via citation, or renaming of known results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical study whose central claims rest on the assumption that coreference resolution produces measurable disambiguation benefits; no free parameters, invented entities, or non-standard mathematical axioms are visible from the abstract.

axioms (1)
  • domain assumption Coreference resolution tools can accurately replace ambiguous references with explicit entities in retrieved documents
    The improvement claims presuppose that the resolution step itself is reliable and does not introduce new errors.

pith-pipeline@v0.9.0 · 5743 in / 1285 out tokens · 96086 ms · 2026-05-19T05:27:35.461765+00:00 · methodology

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Reference graph

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