Papers by Tyler McDonald
NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers (2025.coling-main)
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| Challenge: | Large Language Models have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. |
| Approach: | They propose to penalize quick, intuitive "System 1" thinking by combining linguistic isolation with resistance to intuitive shortcuts to assess model's reasoning abilities. |
| Outcome: | The proposed model penalizes quick, intuitive “System 1” thinking, isolating fundamental reasoning skills. |
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index (2025.coling-main)
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| Challenge: | Prompt engineering is a growing subdiscipline of natural language processing . a lack of appropriate consideration for the financial constraints of computationally burdensome methods can limit their adoption and impact. |
| Approach: | They propose a new metric that combines accuracy scores with token consumption to reflect different resource constraints. |
| Outcome: | The economic prompting index (EPI) measures the performance of 6 prompting techniques across 10 widely-used language models and 4 diverse datasets. |
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions (2024.emnlp-main)
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| Challenge: | Existing models that assess explicit and implicit biases are based on a single scenario . a dataset of 450 offensive progressions contains 2,700 sentences of varying severity . |
| Approach: | They evaluate a dataset of offensive progressions that contain 2,700 sentences . they find that even the best-performing models detect bias inconsistently . |
| Outcome: | The proposed dataset shows that even the best-performing models detect bias inconsistently . aligning models with human judgments on STOP can improve answer rates on sensitive tasks by 191% . |
Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition (2024.acl-srw)
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| Challenge: | Trace-of-Thought Prompting allows small neural networks to emulate larger, teacher models with reduced computational demands. |
| Approach: | They propose a framework to distill critical reasoning capabilities from teacher models to student models . they use problem decomposition to enhance interpretability and facilitate human-in-the-loop interventions . |
| Outcome: | a new framework enables small neural networks to emulate the performance of larger, teacher models . it leverages problem decomposition to enhance interpretability and facilitate human-in-the-loop interventions . the proposed framework is available on github.com/trace-of-thought/trac-of_thought-prompting/main . |