Papers by Ramya Keerthy Thatikonda
Logical Reasoning with Outcome Reward Models for Test-Time Scaling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), but it is under-explored in deductive reasoning. |
| Approach: | They propose to use Chain-of-Thought to generate data using single and multiple samples to train ORMs. |
| Outcome: | The proposed model expands the type of errors covered in the training dataset, covering previously unexplored error types. |
Assessing the Sensitivity and Alignment of FOL Closeness Metrics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent paradigm of logical reasoning with tool-augmented large language models has been successful, but the correctness of FOL statements often go unverified due to the lack of a reliable evaluation metric. |
| Approach: | They compare sensitivity of existing metrics and their alignment with LLM to evaluate FOLs. |
| Outcome: | The proposed metrics improve the sensitivity and robustness of the proposed models. |