Papers by Pradyot Prakash
Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)
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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang
| Challenge: | Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models. |
| Approach: | They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. |
| Outcome: | The proposed framework improves the factuality of LM generators by enhancing their training data. |
Dynamic Strategy Planning for Efficient Question Answering with Large Language Models (2025.findings-naacl)
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| Challenge: | Existing studies have shown that using a single fixed strategy for answering all different kinds of questions is sub-optimal in performance and inefficient in terms of generated tokens and retrievals. |
| Approach: | They propose a technique to induce a dynamic strategy selection process in Large Language Models (LLMs) by incorporating an initial decision step to select the most suitable strategy conditioned on the input question and guides the LLM’s response generation accordingly. |
| Outcome: | The proposed technique improves model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model. |