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REVIEW 2 major objections 2 minor 5 references

LLMs extract only a subset of the implicit inferences that humans identify in text, with differences in caution depending on context type.

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T0 review · grok-4.3

2026-05-10 06:54 UTC

load-bearing objection The paper's comparison of LLMs and humans on implicit inferences is interesting but undermined by weak details on the human annotation process. the 2 major comments →

arxiv 2604.17085 v1 submitted 2026-04-18 cs.CL cs.AI

Comparing Human and Large Language Model Interpretation of Implicit Information

Antonio De Santis , Tommaso Bonetti , Andrea Tocchetti , Marco Brambilla This is my paper
classification cs.CL cs.AI
keywords implicit information extractionlarge language modelshuman comparisonrelational tripletsknowledge graphsinference conservatismcrowdsourced evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper sets up a new task to measure how well large language models can extract unspoken information from sentences and turn it into structured knowledge. It runs two models through a pipeline that pulls out relational triplets, checks which ones are implicit, and compares the results to what crowds of people suggest for the same sentences. Humans match many of the model outputs but regularly add extra inferences the models overlooked, pointing to gaps in current LLM capabilities for handling implicit meaning. The comparison also shows models tend to avoid social inferences more than people do, but people avoid them more when the text is brief and factual.

Core claim

The paper establishes that LLM-based implicit information extraction pipelines produce relational triplets that humans largely agree with but find incomplete, as humans propose numerous additional inferences; furthermore, models exhibit greater conservatism in socially rich contexts compared to humans, who show more conservatism in shorter factual contexts.

What carries the argument

The LLM-based IIE pipeline, which extracts relational triplets from a context sentence, validates implicit inferences, and analyzes temporal relations to construct a knowledge graph.

Load-bearing premise

That the crowdsourced human annotations provide a reliable and unbiased standard for what counts as valid implicit information in the given sentences.

What would settle it

Running the same pipeline and human annotation process on a new dataset where models and humans produce statistically similar numbers of additional inferences would challenge the claim of limited LLM coverage.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • LLM systems will require additional steps to achieve complete coverage of human-like implicit inferences.
  • Context type influences the reliability of implicit extraction, with social contexts favoring human over model inferences.
  • Knowledge graphs built from LLM outputs alone may miss key relational details present in human interpretations.
  • Evaluation of LLMs on implicit tasks should account for both agreement and the volume of additional human suggestions.

Where Pith is reading between the lines

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

  • Training LLMs on datasets that include human-proposed implicit additions could improve their coverage in future iterations.
  • Applications involving dialogue or narrative understanding may need hybrid human-model systems to capture full implicit content.
  • The observed patterns of conservatism suggest that model behavior could be tuned differently for social versus factual domains.
  • Testing the pipeline on more diverse datasets might reveal whether the limited coverage is a general limitation or specific to the chosen contexts.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the Implicit Information Extraction (IIE) task and an LLM-based pipeline that extracts relational triplets from context sentences, validates implicit inferences, and analyzes temporal relations. It evaluates two LLMs against crowdsourced human judgments on two datasets, claiming that humans agree with most model triplets but consistently propose many additions (indicating limited LLM coverage) and that models are more conservative about implicit inferences than humans in socially rich contexts while humans are more conservative in shorter, fact-oriented contexts.

Significance. If the central empirical comparisons hold after methodological clarification, the work would usefully document gaps in current LLM handling of implicit information relative to humans and identify context-type dependencies in inference conservatism. The public release of code at the cited GitHub repository is a clear strength supporting reproducibility. The overall significance remains moderate because the claims rest on unverified details of the human benchmark.

major comments (2)
  1. [Abstract and evaluation] Abstract and evaluation description: the reported findings on human-model agreement and context-dependent conservatism rest on crowdsourced judgments, yet the manuscript provides no information on dataset construction, number of annotators per item, inter-annotator agreement statistics, annotation guidelines, or statistical tests. Without these, it is impossible to determine whether the 'many additions' and conservatism differences reflect genuine model shortcomings or annotator variability and differing interpretations of 'implicit'.
  2. [IIE pipeline and experiments] Pipeline and comparison methodology: the triplet extraction, validation, and temporal analysis steps are described at a high level, but the manuscript does not report controls for prompt sensitivity, model choice effects, or explicit criteria used to decide when a human-proposed triplet counts as a true implicit inference missing from the model output. These omissions directly affect the load-bearing claim of limited LLM coverage.
minor comments (2)
  1. [Abstract] The abstract uses the term 'conservative' without a brief operational definition; adding one sentence would improve immediate clarity for readers.
  2. [Results] Figure or table captions describing the two datasets should explicitly state their sizes, domains, and how they were selected to support the social vs. fact-oriented contrast.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript accordingly to provide the requested methodological details.

read point-by-point responses

  1. Referee: [Abstract and evaluation] Abstract and evaluation description: the reported findings on human-model agreement and context-dependent conservatism rest on crowdsourced judgments, yet the manuscript provides no information on dataset construction, number of annotators per item, inter-annotator agreement statistics, annotation guidelines, or statistical tests. Without these, it is impossible to determine whether the 'many additions' and conservatism differences reflect genuine model shortcomings or annotator variability and differing interpretations of 'implicit'.

    Authors: We agree that these details are necessary to fully evaluate the human benchmark and the reported differences. The current manuscript omitted an expanded description of the crowdsourcing protocol for space reasons. In the revision we will add a dedicated subsection under Experiments that specifies dataset construction and sources, the number of annotators per item, inter-annotator agreement statistics, the annotation guidelines, and the statistical tests used to compare human and model outputs. This will allow readers to assess whether the observed patterns reflect genuine differences rather than annotator variability. revision: yes

  2. Referee: [IIE pipeline and experiments] Pipeline and comparison methodology: the triplet extraction, validation, and temporal analysis steps are described at a high level, but the manuscript does not report controls for prompt sensitivity, model choice effects, or explicit criteria used to decide when a human-proposed triplet counts as a true implicit inference missing from the model output. These omissions directly affect the load-bearing claim of limited LLM coverage.

    Authors: We acknowledge that the pipeline description is currently high-level. In the revised manuscript we will expand the IIE pipeline section to report (i) the specific prompts employed and results of prompt-sensitivity checks, (ii) the rationale for the two chosen LLMs together with any observed model-choice effects, and (iii) the explicit matching criteria (a combination of embedding-based semantic similarity and manual adjudication) used to determine when a human-proposed triplet constitutes a missing implicit inference. These additions will strengthen the evidence for limited LLM coverage. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison with independent human annotations

full rationale

The paper conducts an empirical evaluation of an LLM-based IIE pipeline against crowdsourced human judgments on two datasets, reporting agreement rates, proposed additions, and context-dependent differences in conservatism. No mathematical derivations, fitted parameters, or predictions appear; results follow directly from model outputs versus separate human annotations. No self-citations, uniqueness theorems, or ansatzes are invoked to support the central claims. The comparison is self-contained as standard experimental benchmarking without reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on the validity of crowdsourced human judgments as ground truth and the assumption that the LLM pipeline produces comparable implicit extractions, but the abstract specifies no free parameters, mathematical axioms, or new postulated entities.

invented entities (1)
  • Implicit Information Extraction (IIE) task no independent evidence
    purpose: Structured extraction of relational triplets, implicit inferences, and temporal relations from context sentences into knowledge graphs
    New task introduced in the paper to enable the comparison.

pith-pipeline@v0.9.0 · 5446 in / 1301 out tokens · 52689 ms · 2026-05-10T06:54:39.120749+00:00 · methodology

0 comments
read the original abstract

The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To investigate this, we introduce the task of Implicit Information Extraction (IIE) and propose an LLM-based IIE pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. We evaluate two LLMs against crowdsourced human judgments on two datasets. We find that humans agree with most model triplets yet consistently propose many additions, indicating limited coverage in current LLM-based IIE. Moreover, in our experiments, models appear to be more conservative about implicit inferences than humans in socially rich contexts, whereas humans become more conservative in shorter, fact-oriented contexts. Our code is available at https://github.com/Antonio-Dee/IIE_from_LLM.

Figures

Figures reproduced from arXiv: 2604.17085 by Andrea Tocchetti, Antonio De Santis, Marco Brambilla, Tommaso Bonetti.

Figure 1
Figure 1. Figure 1: Comparison of the interpretation of implicit [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed solution pipeline and its stages. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Implicit triplets with various inference types [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Triplet counts grouped by inference type. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relation type proportions for the temporal [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Clusters of respondents for the triplet classi [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Human sentiment regarding discarded triplets [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Clusters of respondents for the model error [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

discussion (0)

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Reference graph

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5 extracted references · 5 canonical work pages

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