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arxiv: 2604.19921 · v1 · submitted 2026-04-21 · 💻 cs.CL

Recognition: unknown

Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

Eduardo Blanco, Farzana Rashid, MohammadHossein Rezaei, Zijie Wang

Pith reviewed 2026-05-10 02:20 UTC · model grok-4.3

classification 💻 cs.CL
keywords negationcommonsense knowledgelanguage modelspre-trainingif-then relationsnatural language understandingautomatic augmentationnegated triples
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The pith

Pre-training language models on commonsense knowledge augmented with negation improves their understanding of negated statements.

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

The paper identifies that large language models struggle with negation even though it is a frequent feature of natural language and commonsense knowledge has not previously been explored in negated form. It introduces an automatic augmentation technique that adds negation to existing commonsense corpora, creating two new collections with over 2 million if-then triples. Pre-training models on these resources produces measurable gains on negation-related understanding tasks. A reader would care because negation is everyday but remains a persistent weakness in current systems, and this method scales the fix without heavy manual labeling. The central object is the set of negated commonsense triples that carry the improvement.

Core claim

Commonsense knowledge with negation is challenging for models. A novel automatic approach augments existing commonsense knowledge corpora with negation to yield two new corpora containing over 2M triples with if-then relations. Pre-training LLMs on these corpora benefits negation understanding.

What carries the argument

Automatic augmentation method that converts existing commonsense if-then triples into their negated counterparts at scale.

If this is right

  • Pre-training on the augmented corpora improves LLMs on tasks that require understanding negation.
  • Two new resources with over 2 million negated if-then triples become available for training.
  • Commonsense knowledge bases can be extended automatically to cover negation.
  • The benefit appears in multiple natural language understanding settings involving negated statements.

Where Pith is reading between the lines

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

  • The same augmentation idea could be tested on other logical features such as modality or quantification to address additional LLM weaknesses.
  • Better negation handling from these triples may reduce errors in downstream applications like question answering or dialogue where negative statements are frequent.
  • Different base commonsense sources could be run through the method to test whether the gains hold across varied starting data.

Load-bearing premise

The automatic augmentation produces valid negated commonsense triples without introducing enough noise or invalid statements to erase the pre-training benefit.

What would settle it

Pre-training experiments that show no gain on negation benchmarks after using the new corpora, or large-scale human checks that find many invalid negated triples.

Figures

Figures reproduced from arXiv: 2604.19921 by Eduardo Blanco, Farzana Rashid, MohammadHossein Rezaei, Zijie Wang.

Figure 1
Figure 1. Figure 1: A commonsense knowledge triple with the In￾tention relation. Negations are added to both if and then events. Adding different negation cues results in new triples that align (same color on both sides) or conflict with (different colors) commonsense knowledge. ATOMIC (Sap et al., 2019) is one of the largest commonsense knowledge corpora with if-then re￾lations, representing commonsense knowledge as a triple… view at source ↗
read the original abstract

Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural language understanding tasks. Commonsense knowledge, on the other hand, despite being a well-studied topic, lacks investigations involving negation. In this work, we show that commonsense knowledge with negation is challenging for models to understand. We present a novel approach to automatically augment existing commonsense knowledge corpora with negation, yielding two new corpora containing over 2M triples with if-then relations. In addition, pre-training LLMs on our corpora benefits negation understanding.

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

2 major / 0 minor

Summary. The paper claims that negation in commonsense knowledge is challenging for LLMs, introduces a novel automatic augmentation method to create two large corpora (>2M negated if-then triples) from existing commonsense resources, and asserts that pre-training LLMs on these corpora enhances negation understanding.

Significance. Should the augmentation yield high-quality data and the pre-training experiments demonstrate robust improvements with proper controls, this resource could help address LLMs' difficulties with negation in NLU. The scale of the corpora is notable, and the focus on commonsense with negation fills a gap in existing resources. Credit is due for the resource creation effort, though its value depends on validation of the data quality and empirical gains.

major comments (2)
  1. Abstract: The abstract states that pre-training on the corpora benefits negation understanding but supplies no quantitative results, baselines, or evaluation details, leaving the central claim unsupported by visible evidence.
  2. Augmentation Method: The automatic augmentation may generate invalid negated triples (e.g., logical errors or preserved polarity). Without a detailed error analysis or human evaluation in the relevant section quantifying noise levels, it is unclear if pre-training gains stem from negation learning or artifacts of data volume.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the major comments point by point below. Where the comments identify gaps in the current manuscript, we will revise accordingly to strengthen the presentation of our contributions.

read point-by-point responses

  1. Referee: Abstract: The abstract states that pre-training on the corpora benefits negation understanding but supplies no quantitative results, baselines, or evaluation details, leaving the central claim unsupported by visible evidence.

    Authors: We agree that the abstract should provide concrete evidence for the central claim. The full paper contains quantitative results from pre-training experiments, including specific improvements on negation understanding benchmarks relative to baselines. In the revised manuscript, we will update the abstract to include key quantitative findings (e.g., performance gains and evaluation setup) so that the claim is supported at a glance. revision: yes

  2. Referee: Augmentation Method: The automatic augmentation may generate invalid negated triples (e.g., logical errors or preserved polarity). Without a detailed error analysis or human evaluation in the relevant section quantifying noise levels, it is unclear if pre-training gains stem from negation learning or artifacts of data volume.

    Authors: We acknowledge that a quantitative assessment of data quality is important for interpreting the pre-training results. Our augmentation procedure incorporates logical checks to invert polarity correctly, but the initial submission did not include a dedicated human evaluation or error analysis section. We will add this analysis in the revision, reporting inter-annotator agreement and estimated noise rates on a sampled subset of the generated triples. This will allow readers to assess whether observed gains derive primarily from improved negation handling. revision: yes

Circularity Check

0 steps flagged

No significant circularity in resource creation and empirical evaluation

full rationale

The paper centers on creating negated commonsense knowledge resources via automatic augmentation of existing corpora (yielding >2M if-then triples) and then empirically demonstrating pre-training benefits for negation understanding in LLMs. No derivation chain, equations, or predictions exist that reduce to fitted inputs or self-referential definitions. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked to force results. The work is self-contained as an empirical contribution whose validity rests on data quality and experimental outcomes rather than any circular reduction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that automatic negation augmentation preserves commonsense validity and that observed gains stem specifically from the negated content rather than dataset size or other artifacts.

axioms (1)
  • domain assumption Existing commonsense knowledge corpora can be reliably augmented with negation using automatic methods without introducing invalid triples.
    Invoked in the description of the novel augmentation approach that yields the new corpora.

pith-pipeline@v0.9.0 · 5397 in / 986 out tokens · 40046 ms · 2026-05-10T02:20:28.435045+00:00 · methodology

discussion (0)

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