Papers by Prakamya Mishra
TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games (2025.emnlp-main)
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| Challenge: | Recent advances in large reasoning models (LRMs) have driven significant breakthroughs across various reasoning tasks including deductive, arithmetic, commonsense, relational, and symbolic reasoning. |
| Approach: | They propose a programmatic approach to evaluate basic strategic, spatial, and logical reasoning abilities in large reasoning models through four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. |
| Outcome: | The proposed model performs 41% lower on TTT-Bench than MATH 500 and AIME 2024 models, while the larger models perform better on longer reasoning traces. |
Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction (2024.findings-eacl)
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| Challenge: | Scientific domain-specific pre-training has led to state-of-the-art keyphrase extraction performance with a majority of benchmarks being within the domain. |
| Approach: | They propose to leverage topic information available in the data to build a clustering-based sampling approach that facilitates selecting a few samples to label from the target domain . |
| Outcome: | The proposed approach leads to 26.35 points in performance when compared to selecting few-shot samples uniformly at random. |
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences. |
| Approach: | They propose a pipeline that leverages >100B parameter GPT variants to act as synthetic experts to generate edit feedback without additional human annotations. |
| Outcome: | The proposed pipeline aims to improve the quality of clinical note summarizations by generating edit feedback without human annotations. |