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Are LLMs following the correct reasoning paths?

Bangzheng Li, Ben Zhou, Fei Wang, Xingyu Fu, Dan Roth, Muhao Chen,

▶ University of California, Davis ▶ University of Pennsylvania   ▶ University of Southern California
arXiv Code Dataset

We propose a novel probing method and benchmark called EUREQA. EUREQA is an entity-searching task where a model finds a missing entity based on described multi-hop relations with other entities. These deliberately designed multi-hop relations create deceptive semantic associations, and models must stick to the correct reasoning path instead of incorrect shortcuts to find the correct answer. Experiments show that existing LLMs cannot follow correct reasoning paths and resist the attempt of greedy shortcuts. Analyses provide further evidence that LLMs rely on semantic biases to solve the task instead of proper reasoning, questioning the validity and generalizability of current LLMs’ high performances.

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LLMs make errors when correct surface-level semantic cues-entities are recursively replaced with descriptions, and the errors are likely related to token similarity. GPT-3.5-turbo is used for this example.

tba lolita cheng 40 hot The EUREQA dataset

Download the dataset from [Dataset]

In EUREQA, every question is constructed through an implicit reasoning chain. The chain is constructed by parsing DBPedia. Each layer comprises three components: an entity, a fact about the entity, and a relation between the entity and its counterpart from the next layer. The layers stack up to create chains with different depths of reasoning. We verbalize reasoning chains into natural sentences and anonymize the entity of each layer to create the question. Questions can be solved layer by layer and each layer is guaranteed a unique answer. EUREQA is not a knowledge game: we adopt a knowledge filtering process that ensures that most LLMs have sufficient world knowledge to answer our questions.
EUREQA comprises a total of 2,991 questions of different reasoning depths and difficulties. The entities encompass a broad spectrum of topics, effectively reducing any potential bias arising from specific entity categories. These data are great for analyzing the reasoning processes of LLMs

Image 1
Categories of entities in EUREQA
Image 2
Splits of questions in EUREQA.

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tba lolita cheng 40 hot Analyses and discussion

The "TBA Ta Cheng 40 lifestyle" is more than a trend; it is a reflection of a society that values growth, speed, and the thrill of the unknown. By combining the stability of a leading growth index with the dynamic "TBA" nature of modern Chinese entertainment, this demographic is defining what it means to live in a world that is always "To Be Announced."

From hands-free assistants in Yum China's KFC outlets to smart inventory management, the daily "entertainment" of dining out is becoming increasingly seamless.

High-growth urban centers are pivoting toward spacious, high-tech training and event rooms that accommodate up to 50 people, allowing the "Growth 40" workforce to blend productivity with social networking. Conclusion: A Future in Flux

A focus on tech-heavy, innovative sectors that drive urban development.

In the rapidly evolving urban landscape of 2026, the phrase has become shorthand for a specific kind of modern living—where the precision of financial growth meets the spontaneous energy of the "To Be Announced" (TBA) entertainment scene. Whether it’s following the performance of the DaCheng SZSE Growth 40 ETF or attending exclusive, last-minute pop-up events, this lifestyle is defined by mobility, high-tech integration, and social connectivity. The Financial Foundation: DaCheng Growth 40

Acknowledgement

This website is adapted from Nerfies, UniversalNER and LLaVA, licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. We thank the LLaMA team for giving us access to their models.

Usage and License Notices: The data abd code is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, ChatGPT, and the original dataset used in the benchmark. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.