Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity

Haojie Li; Hongyu Liu; Ruohan Li; Xinwei Wu; Xinyu Ji; Yigeng Zhang; Yule Chen

arxiv: 2507.23121 · v2 · submitted 2025-07-30 · 💻 cs.CL · cs.AI

Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity

Xinwei Wu , Haojie Li , Hongyu Liu , Xinyu Ji , Ruohan Li , Yule Chen , Yigeng Zhang This is my paper

Pith reviewed 2026-05-19 01:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords LLM trustworthinessChinese textual ambiguitylinguistic uncertaintybenchmark datasetambiguous text detectionoverconfidence in LLMslanguage model limitations

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The pith

LLMs fail to detect Chinese textual ambiguity, overconfidently treating ambiguous sentences as having one clear meaning while overthinking alternatives.

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

The paper tests how large language models respond to ambiguous Chinese narrative text by building a benchmark of ambiguous sentences paired with disambiguated versions, grouped into three main categories and nine subcategories. Experiments show that models cannot reliably tell ambiguous text from unambiguous text, assign single meanings with excessive confidence, and engage in unnecessary overthinking about possible interpretations, behaviors that diverge from how humans handle uncertainty. A sympathetic reader cares because everyday language contains frequent ambiguity in areas such as instructions, news, and conversations, and unreliable handling could produce flawed outputs in deployed systems. The work therefore points to a core limitation in current LLMs' capacity to represent and manage linguistic uncertainty rather than resolve it prematurely.

Core claim

By constructing and annotating a dataset of Chinese ambiguous sentences with context and corresponding disambiguated pairs across three main categories and nine subcategories, the authors demonstrate that LLMs cannot reliably distinguish ambiguous text from unambiguous text, exhibit overconfidence by interpreting ambiguous text as possessing a single meaning, and display overthinking when attempting to enumerate multiple possible meanings, behaviors that differ substantially from human responses to the same material.

What carries the argument

The benchmark dataset of collected and generated ambiguous Chinese sentences with context and disambiguated pairs, systematically organized into three main categories and nine subcategories, used to probe model behavior on ambiguity.

If this is right

Deployed LLMs may generate incorrect or one-sided responses in real-world settings containing common linguistic ambiguity.
Overconfidence in single interpretations could produce errors in high-stakes applications such as legal analysis or medical communication.
Overthinking multiple meanings may raise computational costs without improving accuracy on ambiguous inputs.
The introduced dataset provides a concrete testbed for developing methods that better represent uncertainty in language understanding.

Where Pith is reading between the lines

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

The observed fragility may appear in other languages that share similar sources of ambiguity, suggesting a broader architectural gap in current models.
Training objectives that reward explicit representation of multiple interpretations rather than early resolution could reduce the reported overconfidence.
Future evaluation suites should measure not only accuracy but also calibration of uncertainty estimates when ambiguity is present.

Load-bearing premise

The collected and generated ambiguous sentences with their disambiguated pairs, organized into the stated categories, form a representative sample of real Chinese textual ambiguity and human interpretations of it.

What would settle it

A follow-up experiment in which LLMs correctly flag a large new sample of Chinese ambiguous sentences as having multiple meanings, list those meanings without overconfidence, and avoid overthinking at rates matching human performance would challenge the central claim.

read the original abstract

In this work, we study a critical research problem regarding the trustworthiness of large language models (LLMs): how LLMs behave when encountering ambiguous narrative text, with a particular focus on Chinese textual ambiguity. We created a benchmark dataset by collecting and generating ambiguous sentences with context and their corresponding disambiguated pairs, representing multiple possible interpretations. These annotated examples are systematically categorized into 3 main categories and 9 subcategories. Through experiments, we discovered significant fragility in LLMs when handling ambiguity, revealing behavior that differs substantially from humans. Specifically, LLMs cannot reliably distinguish ambiguous text from unambiguous text, show overconfidence in interpreting ambiguous text as having a single meaning rather than multiple meanings, and exhibit overthinking when attempting to understand the various possible meanings. Our findings highlight a fundamental limitation in current LLMs that has significant implications for their deployment in real-world applications where linguistic ambiguity is common, calling for improved approaches to handle uncertainty in language understanding. The dataset and code are publicly available at this GitHub repository: https://github.com/ictup/LLM-Chinese-Textual-Disambiguation.

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

This paper builds a categorized Chinese ambiguity benchmark and reports LLMs mishandle it unlike humans, but the abstract leaves the data validation and experiment details too thin to judge the strength of that finding.

read the letter

This paper builds a new benchmark of Chinese ambiguous sentences with disambiguated pairs, organized into three main categories and nine subcategories, then tests LLMs on them. The headline result is that the models cannot reliably spot ambiguity, default to single interpretations with high confidence, and overthink the options in ways that differ from human readers. They release the dataset and code, which is a practical step forward for anyone who wants to probe these issues further. Focusing on Chinese is also useful, since most existing ambiguity work stays in English and some ambiguity patterns may differ across languages. That combination gives the work a clear empirical hook. The soft spots sit in the methods. The abstract says the examples were collected and generated but gives no information on how ambiguity was confirmed with native speakers, what inter-annotator agreement looked like, or whether the sentences reflect naturally occurring text rather than constructed cases. Without those checks, the representativeness claim that the stress-test note flags remains unverified. The experiments also omit which models were tested, how prompts were designed, what metrics captured overconfidence or overthinking, and whether any statistical controls were used. Those gaps make it difficult to tell whether the reported behaviors are robust or sensitive to small changes in setup. This is the kind of paper that would interest researchers working on LLM robustness and trustworthiness, especially in multilingual settings. A reader who needs concrete examples of linguistic uncertainty to test against could get value from the dataset itself even before the claims are tightened. It deserves a serious referee because the topic is practically relevant and the authors have produced shareable data rather than just another opinion piece. I would send it to review with a request for more on data construction and experimental controls.

Referee Report

2 major / 1 minor

Summary. The manuscript claims to study LLM trustworthiness on ambiguous Chinese narrative text. It describes creating a benchmark by collecting and generating ambiguous sentences with context and disambiguated pairs, organized into 3 main categories and 9 subcategories. Experiments are said to show that LLMs cannot reliably distinguish ambiguous from unambiguous text, exhibit overconfidence in single interpretations of ambiguous text, and overthink multiple meanings, indicating a fundamental limitation with implications for real-world deployment. The dataset and code are released publicly.

Significance. If the results hold after addressing methodological details, the work would be significant for highlighting LLM limitations in handling linguistic uncertainty, a common issue in applications involving Chinese text such as translation or dialogue systems. The public availability of the dataset and code is a strength that supports reproducibility and community verification.

major comments (2)

[Abstract] Abstract: The reported LLM behaviors (inability to distinguish ambiguous from unambiguous text, overconfidence in single interpretations, and overthinking) are central to the claim of fragility. However, the abstract supplies no information on the LLMs tested, evaluation metrics, prompts or protocols used to measure these behaviors, or any statistical tests and controls.
[Abstract] Abstract: The benchmark dataset is load-bearing for attributing observed behaviors to a fundamental LLM limitation rather than data artifacts. The abstract states that examples were collected, generated, and annotated into 3 main and 9 subcategories but provides no details on how ambiguity was confirmed by native speakers, how human interpretations were elicited or validated, inter-annotator agreement, or whether the categories reflect naturally occurring Chinese ambiguity.

minor comments (1)

[Abstract] Abstract: The term 'overthinking' is introduced without an operational definition or concrete example of the behavior being measured.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that the abstract would benefit from additional methodological details to better support the central claims. We address each major comment below and will revise the abstract in the next version of the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: The reported LLM behaviors (inability to distinguish ambiguous from unambiguous text, overconfidence in single interpretations, and overthinking) are central to the claim of fragility. However, the abstract supplies no information on the LLMs tested, evaluation metrics, prompts or protocols used to measure these behaviors, or any statistical tests and controls.

Authors: We agree that the abstract should include these key experimental details to allow readers to properly evaluate the reported behaviors. In the revised abstract, we will specify the LLMs tested, the primary evaluation metrics (such as distinction accuracy between ambiguous and unambiguous text and measures of overconfidence), the prompting protocols employed, and reference to the statistical tests and controls used in the analysis. revision: yes
Referee: [Abstract] Abstract: The benchmark dataset is load-bearing for attributing observed behaviors to a fundamental LLM limitation rather than data artifacts. The abstract states that examples were collected, generated, and annotated into 3 main and 9 subcategories but provides no details on how ambiguity was confirmed by native speakers, how human interpretations were elicited or validated, inter-annotator agreement, or whether the categories reflect naturally occurring Chinese ambiguity.

Authors: We acknowledge the need for greater transparency on dataset construction in the abstract. We will revise the abstract to summarize how ambiguity was validated by native speakers, how human interpretations were elicited and cross-validated, the inter-annotator agreement achieved, and that the 3 main and 9 subcategories were derived from naturally occurring patterns of ambiguity in Chinese narrative text. These details are elaborated in the main body of the paper. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark construction and model evaluation

full rationale

The paper constructs a benchmark by collecting and generating ambiguous Chinese sentences with disambiguated pairs, organizes them into 3 main categories and 9 subcategories, and reports LLM behaviors observed on this data. The abstract contains no equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations that reduce any result to its inputs by construction. All claims follow directly from running the described experiments on the created dataset, which is released publicly, making the work a standard self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the representativeness of the constructed benchmark and the validity of the chosen evaluation tasks as proxies for human-like handling of ambiguity; no free parameters or invented entities are described.

axioms (1)

domain assumption Ambiguous Chinese sentences with context can be systematically collected or generated and paired with disambiguated versions that represent distinct interpretations.
This premise underpins the creation of the benchmark dataset used for all experiments.

pith-pipeline@v0.9.0 · 5714 in / 1216 out tokens · 50820 ms · 2026-05-19T01:45:44.398420+00:00 · methodology

discussion (0)

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We created a benchmark dataset by collecting and generating ambiguous sentences with context and their corresponding disambiguated pairs... categorized into 3 main categories and 9 subcategories.

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Forward citations

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