Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation

Adam Dejl; Alessandra Pascale; James Barry; Javier Carnerero Cano

arxiv: 2510.07926 · v2 · submitted 2025-10-09 · 💻 cs.CL

Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation

Adam Dejl , James Barry , Alessandra Pascale , Javier Carnerero Cano This is my paper

Pith reviewed 2026-05-18 09:15 UTC · model grok-4.3

classification 💻 cs.CL

keywords comprehensiveness evaluationfactual recalltext generationLLM metricsmissing information detectionautomatic evaluationnatural language inferencequestion answering

0 comments

The pith

A direct end-to-end LLM metric detects missing content in generated texts more effectively than NLI-based or Q&A-based alternatives.

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

Large language models frequently omit key information when producing responses from multiple sources, and such gaps can lead to harm comparable to outright factual errors. The paper tests three automated ways to measure how complete these outputs are. One breaks source and generated texts into atomic facts and uses natural language inference to spot absences. Another pulls out question-answer pairs and checks consistency across sources. The third simply prompts an LLM to name what is missing. Experiments show the plain end-to-end prompt performs best overall, although it supplies less detailed breakdowns and is less robust than the decomposed approaches. The study also applies the metrics to measure how comprehensively several open-weight models answer queries when given multiple documents.

Core claim

The paper establishes that an end-to-end approach, which directly asks a large language model to identify missing or underrepresented information in generated text, proves more effective at evaluating comprehensiveness than either an NLI pipeline that decomposes statements or a Q&A pipeline that compares extracted pairs. This effectiveness holds across responses from multiple open-weight LLMs to user queries grounded in several sources, although the direct method trades away some robustness, interpretability, and result granularity.

What carries the argument

The end-to-end LLM prompt that directly identifies missing content without first decomposing texts into facts or questions.

If this is right

Direct LLM prompts can serve as a practical tool for automatically flagging incomplete factual recall in text generation.
Open-weight LLMs differ measurably in how comprehensively they synthesize answers from multiple sources.
NLI and Q&A decomposition methods yield finer-grained outputs but deliver lower overall effectiveness than the direct approach.
Comprehensiveness evaluation can now be applied at scale to assess LLM responses without manual review.

Where Pith is reading between the lines

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

Simpler metrics may become the default choice for routine recall checks even when more structured alternatives exist.
The same direct-prompt technique could be tested on generation tasks outside query answering, such as summarization or report writing.
Hybrid systems that use the end-to-end method first and then apply decomposition only to flagged gaps might recover some lost interpretability.

Load-bearing premise

The chosen evaluation setup with multiple sources and user queries serves as a valid proxy for real-world comprehensiveness, and LLM judgments of missing content align with human notions of completeness.

What would settle it

A human rating study on the same set of generated texts that measures how often experts mark the same omissions as the end-to-end metric versus the NLI or Q&A metrics would show whether the performance advantage reflects actual completeness.

Figures

Figures reproduced from arXiv: 2510.07926 by Adam Dejl, Alessandra Pascale, James Barry, Javier Carnerero Cano.

**Figure 2.** Figure 2: Overview of the three comprehensiveness metrics introduced in this work [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Results of comprehensiveness meta-evaluation on the WikiContradict dataset for different models and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Results of comprehensiveness meta-evaluation on the ConflictBank dataset for different models and metric [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Averaged results of comprehensiveness meta-evaluation on both datasets for different models and metric [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Results of response comprehensiveness evalu [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Definition of the label match rate (LMR) score variant used for comprehensiveness meta-evaluation on the [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

read the original abstract

Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce outputs that are incomplete or selectively omit key information. In sensitive domains, such omissions can result in significant harm comparable to that posed by factual inaccuracies, including hallucinations. In this study, we address the challenge of evaluating the comprehensiveness of LLM-generated texts, focusing on the detection of missing information or underrepresented viewpoints. We investigate three automated evaluation metrics: (1) an NLI-based method that decomposes texts into atomic statements and uses natural language inference (NLI) to identify missing facts, (2) a Q&A-based metric that extracts question-answer pairs and compares responses across sources, and (3) an end-to-end approach that directly identifies missing content using LLMs. Our experiments demonstrate the surprising effectiveness of the simple end-to-end metric compared to more complex metrics, though at the cost of reduced robustness, interpretability and result granularity. We further assess the comprehensiveness of responses from several popular open-weight LLMs when answering user queries based on multiple sources.

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.

Desk Editor's Note private letter to a colleague

The paper compares three metrics for spotting missing information in LLM outputs and finds the plain end-to-end LLM approach works best on their tests, but the ranking lacks human grounding.

read the letter

The main thing to know is that this work tests three ways to measure how complete LLM answers are when they draw from multiple sources, and reports that a direct LLM prompt for missing content beats both an NLI decomposition and a Q&A-pair method. They also run the metrics on a few open-weight models to show real differences in output coverage. That focus on omissions as a distinct risk from hallucinations is useful, especially for domains where leaving things out can cause harm. The setup with user queries and multiple sources feels practical rather than contrived. The experiments give a clear ranking and note the trade-offs in robustness and granularity for the simpler metric. The soft spot is exactly the one the stress-test flags. The superiority claim comes from using an LLM to label what counts as missing, with no human annotations or agreement numbers to show those labels track actual reader judgments of completeness. Without that check, the result could just reflect the judge model favoring outputs that look like its own style. The abstract is also thin on dataset details, exact baselines, and statistical controls, so the full paper needs to fill those in to make the comparisons convincing. This is for people building or choosing evaluation tools for retrieval-augmented or multi-document generation. A reader working on LLM reliability could borrow the metric ideas or the comparison framework, though they would probably add their own human validation step. It deserves peer review because the problem is real, the three-way comparison is concrete, and the authors have done the legwork even if the current evidence for the ranking is not yet anchored well enough.

Referee Report

2 major / 2 minor

Summary. The paper introduces three automated metrics for evaluating comprehensiveness (detection of missing information or underrepresented viewpoints) in LLM-generated texts: (1) an NLI-based method that decomposes text into atomic statements and applies natural language inference, (2) a Q&A-based metric that extracts question-answer pairs and compares across sources, and (3) a simple end-to-end LLM approach that directly identifies missing content. Experiments on responses to user queries drawn from multiple sources show the end-to-end metric to be surprisingly effective relative to the more complex alternatives, at the expense of robustness, interpretability, and granularity; the work also reports comprehensiveness assessments for several popular open-weight LLMs.

Significance. If the effectiveness ranking holds under proper external validation, the work would supply a practical, low-complexity tool for automatic assessment of factual recall and completeness in generated text—an area of growing importance given the harm that selective omissions can cause in sensitive domains. The explicit comparison of metric families and the trade-off analysis would be useful for practitioners choosing evaluation methods.

major comments (2)

[Experiments] Experiments section: the central claim that the end-to-end LLM metric outperforms the NLI-decomposition and Q&A-pair metrics rests on LLM-generated labels for missing content, yet the manuscript reports no human annotations, Pearson/Spearman correlations with human judgments, or inter-annotator agreement figures. Without this external anchor, the reported superiority may reflect shared LLM biases rather than genuine improvement in measuring comprehensiveness.
[Experimental setup] Experimental setup (and abstract): no details are supplied on the concrete datasets or corpora, the number and selection criteria for user queries, the exact baselines or controls, or any statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) used to support the ranking of the three metrics. These omissions make the effectiveness claim difficult to reproduce or assess for robustness.

minor comments (2)

[Metric definitions] Notation for the three metrics is introduced without a compact summary table that would allow quick comparison of their inputs, outputs, and computational requirements.
[End-to-end metric] The manuscript should clarify whether the same LLM family is used both for generation and for the end-to-end judge, and if so, whether any steps were taken to mitigate self-referential bias.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback on our manuscript. We appreciate the recognition of the work's potential utility for automatic assessment of comprehensiveness in LLM-generated text. We address each major comment below and are prepared to make revisions to enhance reproducibility and address limitations in validation.

read point-by-point responses

Referee: [Experiments] Experiments section: the central claim that the end-to-end LLM metric outperforms the NLI-decomposition and Q&A-pair metrics rests on LLM-generated labels for missing content, yet the manuscript reports no human annotations, Pearson/Spearman correlations with human judgments, or inter-annotator agreement figures. Without this external anchor, the reported superiority may reflect shared LLM biases rather than genuine improvement in measuring comprehensiveness.

Authors: We agree that the lack of human annotations represents a limitation in anchoring the claims. Our experiments rely on LLM-generated labels for missing content to enable a consistent, relative comparison across the three metrics, which highlights the surprising effectiveness of the end-to-end approach alongside its trade-offs in robustness and interpretability. This setup avoids confounding factors from different judges but does carry the risk of shared biases. In revision, we will add an explicit discussion of this limitation in the Experiments section and include a small-scale human evaluation on a subset of examples, reporting Pearson/Spearman correlations and inter-annotator agreement to provide an external validation anchor. revision: partial
Referee: [Experimental setup] Experimental setup (and abstract): no details are supplied on the concrete datasets or corpora, the number and selection criteria for user queries, the exact baselines or controls, or any statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) used to support the ranking of the three metrics. These omissions make the effectiveness claim difficult to reproduce or assess for robustness.

Authors: We acknowledge that these details were insufficiently reported, which hinders reproducibility. The experiments used responses to user queries drawn from multiple sources, with queries selected for diversity in topics and complexity. We will revise the Experimental Setup section (and update the abstract if space permits) to specify the concrete datasets/corpora, exact number of queries and selection criteria, baselines/controls, and statistical significance tests (including paired t-tests or bootstrap confidence intervals) supporting the metric rankings. revision: yes

Circularity Check

0 steps flagged

No circularity: metrics defined independently with empirical comparison

full rationale

The paper defines three distinct metrics (NLI decomposition, Q&A extraction, and direct LLM end-to-end detection) without any deriving one from another by construction or renaming. Experimental ranking of their effectiveness on comprehensiveness is an empirical outcome from applying the metrics to LLM outputs against sources, not a fitted parameter or self-referential loop in the definitions themselves. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked to force results. The setup remains self-contained against external benchmarks of the proposed metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical NLP evaluation paper with no mathematical derivations; the abstract introduces no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5728 in / 1059 out tokens · 46104 ms · 2026-05-18T09:15:24.312426+00:00 · methodology

discussion (0)

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unclear
Relation between the paper passage and the cited Recognition theorem.

We investigate three automated evaluation metrics: (1) an NLI-based method that decomposes texts into atomic statements and uses natural language inference (NLI) to identify missing facts, (2) a Q&A-based metric that extracts question-answer pairs and compares responses across sources, and (3) an end-to-end approach that directly identifies missing content using LLMs.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Beyond Precision: Importance-Aware Recall for Factuality Evaluation in Long-Form LLM Generation
cs.CL 2026-04 unverdicted novelty 6.0

An importance-aware recall metric for LLM factuality evaluation reveals models are better at avoiding false claims than covering all relevant facts.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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If the statement contains vague references , minimally revise them with respect to the specific subjects they refer to in the background text

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No changes should be made to the content and no additional information should be added

Each statement should be minimally revised by only resolving vague references . No changes should be made to the content and no additional information should be added

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[7]

Do not provide any additional explanations or comments

Provide each self - contained statement on a separate line starting with "* ". Do not provide any additional explanations or comments . Refer to the following examples to understand the task and the output format . { FEW - SHOT EXAMPLES } Now , please revise the following statements . Background text : { background_text } Statements to be revised : { stat...

work page 1955
[8]

Your task is to identify the relationship \ between them : does the premise entail , contradict , or remain neutral toward the hypothesis ?

You are given a premise and a hypothesis . Your task is to identify the relationship \ between them : does the premise entail , contradict , or remain neutral toward the hypothesis ?

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Your only output must be one of : ( entailment | contradiction | neutral ) without any \ lead - in , sign - off , new lines or any other formatting

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Do not provide any explanation or rationale to your output

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he " - use the full name , e . g

Use the following examples to learn how to do this , and provide your output for the last \ example given . Premise : The weather forecast said it will rain tomorrow . Hypothesis : It will be sunny tomorrow . Output : contradiction Premise : The company hired three new software engineers this month . Hypothesis : The company did not hire any new employees...

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Carefully read the provided question , background texts , and the evaluated answer to the question

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You should only include information that is directly relevant to answering the original question , ignoring any unrelated content

Identify atomic pieces of information from the background texts that are explicitly covered in the evaluated answer . You should only include information that is directly relevant to answering the original question , ignoring any unrelated content . Think step - by - step as you do this , providing brief reasoning under the Reasoning : header

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[14]

Again , only include information that is relevant to answering the original question

Identify atomic pieces of information from the background texts that are missing from the evaluated answer . Again , only include information that is relevant to answering the original question . Think step - by - step in the same way and briefly explain your reasoning under the shared Reasoning : header

work page
[15]

Each atomic statement should be listed as a separate bullet point under'[ Covered statements ]'and'[ Uncovered statements ]'headers as appropriate

Once you have completed your analysis , output two separate lists of covered and uncovered statements from the background texts . Each atomic statement should be listed as a separate bullet point under'[ Covered statements ]'and'[ Uncovered statements ]'headers as appropriate . For each statement , include the list of background text IDs where it appears ...

work page 1991
[16]

Sorry , I don't have any information relevant to the given query

The aircraft provides a massive range of approximately 8 ,000 nautical miles (14 ,800 km ) . While it has become an icon of the skies , it was a commercial failure due to large development costs and limited sales . Evaluated answer : Airbus A380 has defined the ultra - long - range travel . Its versatility and adaptability to various conditions has contri...

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Current applications and challenges in large language models for patient care: A systematic re- view.Communications Medicine, 5(1):26. I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2023. FacTool: Factual- ity detection in generative AI - A tool augmented framework for multi-task...

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FactReasoner: A probabilistic approach to long-form factuality assessment for large language models.CoRR, abs/2502.18573. Meta. 2025. The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettle- moyer, and Hannaneh Hajishirzi. 2023. FA...

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ConflictBank: A benchmark for evaluating the influence of knowledge conflicts in LLMs. InAd- vances in Neural Information Processing Systems 38: Annual Conference on Neural Information Pro- cessing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024. 10 Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to...

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If the statement contains vague references , minimally revise them with respect to the specific subjects they refer to in the background text

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[5] [5]

No changes should be made to the content and no additional information should be added

Each statement should be minimally revised by only resolving vague references . No changes should be made to the content and no additional information should be added

work page

[6] [6]

However , if there are any conjunctive statements , they should be decomposed into multiple atomic units ( e . g . , Democracies treat citizens as equals regardless of their race or religion . -> Democracies treat citizens as equals regardless of their race . , Democracies treat citizens as equals regardless of their religion .) . Avoid adding duplicate s...

work page

[7] [7]

Do not provide any additional explanations or comments

Provide each self - contained statement on a separate line starting with "* ". Do not provide any additional explanations or comments . Refer to the following examples to understand the task and the output format . { FEW - SHOT EXAMPLES } Now , please revise the following statements . Background text : { background_text } Statements to be revised : { stat...

work page 1955

[8] [8]

Your task is to identify the relationship \ between them : does the premise entail , contradict , or remain neutral toward the hypothesis ?

You are given a premise and a hypothesis . Your task is to identify the relationship \ between them : does the premise entail , contradict , or remain neutral toward the hypothesis ?

work page

[9] [9]

Your only output must be one of : ( entailment | contradiction | neutral ) without any \ lead - in , sign - off , new lines or any other formatting

work page

[10] [10]

Do not provide any explanation or rationale to your output

work page

[11] [11]

he " - use the full name , e . g

Use the following examples to learn how to do this , and provide your output for the last \ example given . Premise : The weather forecast said it will rain tomorrow . Hypothesis : It will be sunny tomorrow . Output : contradiction Premise : The company hired three new software engineers this month . Hypothesis : The company did not hire any new employees...

work page 1955

[12] [12]

Carefully read the provided question , background texts , and the evaluated answer to the question

work page

[13] [13]

You should only include information that is directly relevant to answering the original question , ignoring any unrelated content

Identify atomic pieces of information from the background texts that are explicitly covered in the evaluated answer . You should only include information that is directly relevant to answering the original question , ignoring any unrelated content . Think step - by - step as you do this , providing brief reasoning under the Reasoning : header

work page

[14] [14]

Again , only include information that is relevant to answering the original question

Identify atomic pieces of information from the background texts that are missing from the evaluated answer . Again , only include information that is relevant to answering the original question . Think step - by - step in the same way and briefly explain your reasoning under the shared Reasoning : header

work page

[15] [15]

Each atomic statement should be listed as a separate bullet point under'[ Covered statements ]'and'[ Uncovered statements ]'headers as appropriate

Once you have completed your analysis , output two separate lists of covered and uncovered statements from the background texts . Each atomic statement should be listed as a separate bullet point under'[ Covered statements ]'and'[ Uncovered statements ]'headers as appropriate . For each statement , include the list of background text IDs where it appears ...

work page 1991

[16] [16]

Sorry , I don't have any information relevant to the given query

The aircraft provides a massive range of approximately 8 ,000 nautical miles (14 ,800 km ) . While it has become an icon of the skies , it was a commercial failure due to large development costs and limited sales . Evaluated answer : Airbus A380 has defined the ultra - long - range travel . Its versatility and adaptability to various conditions has contri...

work page 1970