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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.

dolby access full better 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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dolby access full better Analyses and discussion

Features a specialized "Performance Mode" that prioritizes immediate positional awareness and suppresses muddy background frequencies.

Enables HDR content on supported screens. 2. Troubleshooting: "Dolby Access Does Not Work Fully" dolby access full better

Download the Dolby Access app from the Microsoft Store on your PC or Xbox. Troubleshooting: "Dolby Access Does Not Work Fully" Download

Is it worth the price? If you own a decent pair of mid-to-high-end headphones, the $15 is one of the cheapest hardware "upgrades" you can make. It breathes new life into gear that might otherwise sound "closed in." The Verdict: Is it Better? It breathes new life into gear that might

Standard stereo audio often fails to deliver an immersive gaming or movie experience. Upgrading your sound configuration with the full version of Dolby Access provides a competitive edge and cinematic depth. This comprehensive guide explains why the premium upgrade outperforms default audio settings, how to configure it, and how to maximize your sound system's capabilities. What is Dolby Access?

Dolby Atmos is an object-based audio technology that allows sound designers to create a three-dimensional sound environment. It adds height and depth to the audio, making it feel more realistic and immersive. With Dolby Atmos, sound can come from anywhere in the room, including above and below the listener.

The full version uses highly advanced Head-Related Transfer Functions (HRTF) to trick your brain into perceiving depth, altitude, and distance through standard stereo headphones. 2. Tailored, Custom Equalizer Profiles

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.