Backroom Casting Best | Ivy Ireland

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.

ivy ireland backroom casting best
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.

ivy ireland backroom casting best 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.

ivy ireland backroom casting best 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

Backroom Casting Best | Ivy Ireland

Today, the adult industry faces significant challenges regarding the preservation of early internet-era content. Compliance regulations, payment processor restrictions, and copyright enforcement have led to the deletion of vast archives from the early 2010s. For enthusiasts tracking down classic scenes featuring performers like Ivy Ireland, navigating this fragmented landscape requires utilizing dedicated archival networks, tube site forums, and specialized digital peer-to-peer databases.

As Ivy Ireland continues to make her mark on the industry, she remains focused on expanding her repertoire. With several projects lined up and a keen eye on innovation within the creative arts, Ivy is poised to take on new challenges. Her experiences in backroom casting have not only shaped her career but also her approach to every role she takes on. ivy ireland backroom casting best

Known for her distinct look—characterized by brown hair and green eyes—Ireland has performed across various popular sub-genres, including BDSM features with specialized networks like Kink.com , as well as mainstream POV and gonzo productions. As Ivy Ireland continues to make her mark

The strength of the BRCC format usually hinges on the first 15 minutes, and Ivy nails this. She comes across as remarkably grounded and unpretentious. Unlike many performers who either oversell the "naive amateur" angle or act too jaded, Ivy strikes a perfect balance. She is conversational, funny, and surprisingly open. You get the sense that she is genuinely excited to be there, which sells the fantasy of the "casting" premise effectively. Known for her distinct look—characterized by brown hair

Major adult tube sites and indexing platforms rely heavily on historical metadata. Because Ireland's scenes generated high engagement metrics initially, algorithms continue to serve her content to users searching for vintage or classic casting-style media.

Ivy bridges the gap between traditional runway/model work and the intimate, narrative‑driven content that backroom casting demands. Brands love her because she can carry a story without a script , making the final product feel authentic.

: Embracing new technology, she has expanded her portfolio into Virtual Reality projects, providing a more immersive experience for her audience and staying at the forefront of media trends.

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.