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"On the Reasoning Capacity of AI Models and How to Quantify It"

Below podcast on this paper is generated with Google's Illuminate.

Are they really reasoning or just good guessers? This paper reveals the truth.

This paper proposes a new way to measure how well LLMs reason, going beyond simple accuracy. It uses positional bias in multiple-choice questions to see if LLMs actually understand the logic or just rely on memorization and guessing.

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Paper - https://arxiv.org/abs/2501.13833

Original Problem 🤔:

→ LLMs perform well on reasoning benchmarks, but their actual reasoning abilities are unclear.

→ Standard accuracy metrics don't reveal how LLMs make decisions.

→ It is important to distinguish between true logical deduction and other cognitive processes like memorization.

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Solution in this Paper 💡:

→ This paper introduces two models to analyze LLM behavior: a Probabilistic Mixture Model (PMM) and Information-Theoretic Consistency (ITC) analysis.

→ The PMM separates LLM responses into reasoning, memorization, and guessing components.

→ The ITC analysis looks at how confident the LLM is in its answers and how it chooses its strategies.

→ Positional bias, where the position of the answer in a multiple-choice question affects the LLM's answer, is used as a test case.

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Key Insights from this Paper 🧐:

→ True reasoning is still difficult for current LLMs.

→ LLMs often rely on memorization and pattern matching instead of logic.

→ Accuracy alone doesn't fully show an LLM's reasoning abilities.

→ LLMs balance different approaches depending on the question.

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Results 💪:

→ Position D in multiple choice questions is treated differently by the model, showing systematic positional bias.

→ Model accuracy is near perfect for questions where the observed accuracy is greater than 0.4.

→ The model predominately relies on memorization (47%), with reasoning and guessing contributing 26% and 27%, respectively.

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