NYU and Google AI researchers demonstrate LLMs’ deductive reasoning using in-context learning and chain-of-thought prompting. They explore LLMs’ ability to generalize to more intricate proofs and identify that in-context examples with unfamiliar deduction principles promote better performance. The findings hint at the need for further understanding of LLMs’ reasoning capabilities. For more details, refer to the Paper on MarkTechPost.
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Advanced Deductive Reasoning in Machine Learning
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The employment of numerous deduction rules and the construction of subproofs allows the complexity of proofs to develop infinitely in many deductive reasoning tasks, such as medical diagnosis or theorem proving. It is not practical to find data to cover guarantees of all sizes due to the huge proof space. Consequently, starting with basic proofs, a general reasoning model should be able to extrapolate to more complicated ones.
A team of NYU and Google AI researchers has demonstrated that LLMs can engage in deductive reasoning when trained with in-context learning (ICL) and chain-of-thought (CoT) prompting. The ability of LLMs to generalize to proofs that are more sophisticated than their demonstrations is the subject of a new study conducted by researchers from New York University, Google, and Boston University.
The group builds upon previous research in two important respects to gauge LLMs’ general deductive reasoning capacity. According to their research, reasoning tasks benefit most from in-context learning when presented with basic examples that illustrate a variety of deduction rules. In-context examples should include deduction principles it is unfamiliar with, such as proof by cases and proof by contradiction. Additionally, these examples should be accompanied by distractors.
According to their findings, CoT can induce OOD reasoning in LLMs that generalize to compositional proofs. These findings indicate that with proper training, smaller models can compete with larger ones in terms of performance.
To further comprehend the ICL and CoT triggering process, the researchers draw attention to a crucial area for future investigation. They are interested in finding out if simpler examples work better, even though the test case is somewhat sophisticated. Additional research is required to understand how to further characterize extrapolation from specific instances.
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