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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.

blackbullchallenge220624anastasialuxxxx1 free 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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blackbullchallenge220624anastasialuxxxx1 free Analyses and discussion

From the rise of short-form video to the "peak TV" era of streaming, here is an exploration of how entertainment content and popular media are evolving and why they matter more than ever. The Shift from Passive Consumption to Active Participation

The Streaming Revolution and the Death of the "Watercooler Moment"

Entertainment content and popular media are the mirrors of our society. They reflect our collective fears, hopes, and curiosities. Whether it’s a 15-second viral dance or a 10-part prestige drama, the media we consume defines the "now." As technology continues to evolve, the way we tell stories will change, but our fundamental human need for connection through entertainment will remain the same.

Algorithms allow platforms to serve highly specific content to niche audiences, ensuring that there is "something for everyone."

The transition from cable television to services like Netflix, Disney+, and HBO Max has fundamentally changed our viewing habits.

For decades, popular media was a one-way street. You sat in a theater, watched a broadcast, or read a magazine. Today, the landscape is defined by .

While we have more choices, the "watercooler moment"—where everyone watches the same show at the same time—is becoming rarer, replaced by viral social media trends that peak and fade within days. The Power of Representation and Global Media

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.