Papers by K. Brent Venable

2 papers
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models (2025.naacl-long)

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Challenge: Existing studies on in-context learning mechanisms are not consistent . current research identifies two main approaches to explain the ICL mechanism .
Approach: They propose a framework for evaluating in-context learning mechanisms by focusing on regression tasks.
Outcome: The proposed framework can solve regression problems and then measure the extent to which the LLM retrieves its internal knowledge versus learning from in-context examples.
Teaching Probabilistic Logical Reasoning to Transformers (2024.findings-eacl)

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Challenge: Existing approaches to reasoning using transformers are limiting, resulting in inconsistent results in arithmetic and QA benchmarks.
Approach: They propose a novel approach that utilizes probabilistic logical rules as constraints in the fine-tuning phase without relying on them in the inference stage.
Outcome: The proposed approach improves the transformer-based language model’s intrinsic reasoning and makes their probabilistic logical reasoning process more explicit and explainable.

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