Yify Repack | Jerry Maguire

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.

jerry maguire yify
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.

jerry maguire yify 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.

jerry maguire yify 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

Yify Repack | Jerry Maguire

Jerry Maguire continues to resonate through its themes of personal growth, professional authenticity, and the "mission statement" of trading security for integrity. The film is celebrated for its blend of sports drama, romance, and corporate satire, featuring iconic lines that define a decade of pop culture.

The film follows Jerry Maguire (Tom Cruise), a top-tier sports agent at Sports Management International who experiences a "moral epiphany". After penning a 25-page mission statement titled "The Things We Think and Do Not Say," which calls for fewer clients and more personal attention, Jerry is promptly fired. jerry maguire yify

Jerry Maguire is a rare film that manages to be a guy’s sports movie and a woman’s romantic drama simultaneously. Cameron Crowe wrote a screenplay that is funny, touching, and smart. It avoids the clichés of standard romantic comedies by focusing on the flaws of the characters. Jerry Maguire continues to resonate through its themes

Jerry Maguire – YIFY Release: A Long Feature

Title: Jerry Maguire
Year: 1996
Director: Cameron Crowe
Cast: Tom Cruise, Cuba Gooding Jr., Renée Zellweger
YIFY / YTS Release Quality: 1080p / 720p BRrip (Blu-ray Rip), ~1.4–2.2 GB file size, AAC 5.1 audio, typically encoded in x264. After penning a 25-page mission statement titled "The

The Moments You Want in HD

Downloading a YIFY copy means you want the file size small, but the quality high. You need those pixels crisp for the iconic scenes:

If you were asking something else — like wanting a solid text review or script of Jerry Maguire — let me know. I can provide a clean script excerpt, plot summary, or famous quotes (e.g., "Show me the money!").

Accessibility: For many, searching for this specific tag is a way to find a reliable "rip" of a classic film without using massive amounts of data. Plot & Impact

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.