Papers by Alexander Radovic
Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation (2025.acl-short)
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Chengwei Qin, Wenxuan Zhou, Karthik Abinav Sankararaman, Nanshu Wang, Tengyu Xu, Alexander Radovic, Eryk Helenowski, Arya Talebzadeh, Aditya Tayade, Sinong Wang, Shafiq Joty, Han Fang, Hao Ma
| Challenge: | Existing methods for detecting hallucination in long-form tasks focus on limited domains or rely heavily on external fact-checking tools, which may not always be available. |
| Approach: | They propose a new paradigm that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection. |
| Outcome: | The proposed method outperforms existing methods for detecting hallucination in open-domain long-form generation and is more accurate than random guessing. |
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. |