Papers by Yashar Moshfeghi
POW: Political Overton Windows of Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems. |
| Approach: | They propose to use the Overton Window as a framework to map the ideological boundaries that a given LLM will espouse, remain neutral on, or refuse to endorse. |
| Outcome: | The proposed methodology reveals political bias in large language models by examining the political stances of models from eight providers. |
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) and multi-modal models (MMs) have demonstrated remarkable capabilities in problem-solving, but their proficiency in tackling geometry math problems has not been thoroughly evaluated. |
| Approach: | They propose a benchmark to evaluate the performance of large language models and multi-modal models in solving geometry math problems. |
| Outcome: | The proposed model achieves 55.67% accuracy on main subset but only 6.00% accuracy on hard subset. |
GOLD: Geometry Problem Solver with Natural Language Description (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for solving geometry math problems struggle with accurately interpreting geometry diagrams, posing a challenge for problem-solving. |
| Approach: | They propose a model that extracts geometric relations from diagrams and converts them into natural language descriptions. |
| Outcome: | The proposed model outperforms the previous best method on the UniGeo dataset by 12.7% and 42.1% in calculation and proving subsets. |