Papers by Zachary Gottesman
EDUMATH: Generating Standards-aligned Educational Math Word Problems (2026.acl-long)
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Bryan R Christ, Penelope Molitz, Beau LeBlond, Zachary Gottesman, Jonathan Kropko, Thomas Hartvigsen
| Challenge: | Math word problems (MWPs) are critical elements of K-12 math education and can be customized to students' interests and ability levels. |
| Approach: | They propose that LLMs can generate MWPs customized to student interests and math education standards by using an open and closed LLM to evaluate over 11,000 MWps and develop a teacher-annotated dataset for standards-aligned educational MWPS generation. |
| Outcome: | The proposed model outperforms existing closed models without training and is more similar to human-written MWPs but prefers customized MWPS with grade school students. |
Inferring Events from Time Series using Language Models (2026.acl-long)
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| Challenge: | Prior work on reasoning about time series in conjunction with natural language has largely overlooked event descriptions and focused on tasks involving just numeric data like trend analysis or anomaly detection. |
| Approach: | They propose a method for generating tasks that test a model’s ability to reason about events associated with time series data based on sports data and develop a benchmarking method. |
| Outcome: | The proposed method can infer unobserved events from time series data, even when providing minimal context. |
Math Neurosurgery: Isolating Language Models’ Math Reasoning Abilities Using Only Forward Passes (2025.acl-long)
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| Challenge: | Math reasoning is an active area of Large Language Model (LLM) research because it is a hallmark of artificial intelligence and has implications in several domains, including math education. |
| Approach: | They propose a method to isolate math-specific parameters in LLMs using only forward passes. |
| Outcome: | The proposed method improves a model's performance on GSM8K and MATH by 4-17% while leaving non-math behavior unaltered. |