Wen Ruixin Rape The Kindergarten Teacher Next Hot Guide

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.

wen ruixin rape the kindergarten teacher next hot
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.

wen ruixin rape the kindergarten teacher next 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.

wen ruixin rape the kindergarten teacher next hot Performance

Here we present the accuracy of ChatGPT, Gemini-Pro and GPT-4 on the hard set of EUREQA across different depths d of reasoning (number of layers in the questions). We evaluate two prompt strategies: direct zero-shot prompt and ICL with two examples. In general, with the entities recursively substituted by the descriptions of reasoning chaining layers, and therefore eliminating surface-level semantic cues, these models generate more incorrect answers. When the reasoning depth increases from one to five on hard questions, there is a notable decline in performance for all models. This finding underscores the significant impact that semantic shortcuts have on the accuracy of responses, and it also indicates that GPT-4 is considerably more capable of identifying and taking advantage of these shortcuts.

depth d=1 d=2 d=3 d=4 d=5
direct icl direct icl direct icl direct icl direct icl
ChatGPT 22.3 53.3 7.0 40.0 5.0 39.2 3.7 39.3 7.2 39.0
Gemini-Pro 45.0 49.3 29.5 23.5 27.3 28.6 25.7 24.3 17.2 21.5
GPT-4 60.3 76.0 50.0 63.7 51.3 61.7 52.7 63.7 46.9 61.9

Wen Ruixin Rape The Kindergarten Teacher Next Hot Guide

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On the other end of the spectrum lies the "pinkwashing" of breast cancer awareness. October is Breast Cancer Awareness Month, and for thirty days, the world is awash in pink ribbons. But critics argue that many of these campaigns have lost the thread of survivor stories entirely. They have replaced trauma with branding.

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The lesson is clear: An awareness campaign without a survivor story is just marketing. The ribbon is not the story. The person wearing the ribbon is the story. Survivor stories shatter silence

For too long, survivors were asked to donate their stories "for the cause." Ethically, if you are using a survivor’s trauma to raise $1 million, that survivor deserves fair compensation for their labor, time, and emotional toll.

For organizations looking to harness the power of survivor stories and awareness campaigns, the line between empowerment and exploitation is razor thin. Here are the four pillars of ethical storytelling.

This report examines the critical intersection of survivor storytelling and public awareness campaigns. In recent years, the paradigm has shifted from viewing survivors as passive victims to recognizing them as empowered agents of change. The report analyzes the methodologies used to share stories, the psychological impact of these narratives on public perception, and the effectiveness of awareness campaigns in driving policy change and resource allocation. Corporations or public figures may use survivor stories

While social media has amplified survivor stories, it has also introduced new dangers. Cancel culture, doxing (releasing private address information), and victim-blaming trolls are daily realities for survivors who go public. Algorithms often suppress "sensitive content," ironically silencing the very stories that need to be heard.

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Online campaigns open survivors to trolling and online harassment.

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.