Papers by Aakriti Agrawal
Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems (2025.findings-emnlp)
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Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang
| Challenge: | Existing approaches to selecting reliable responses from multiple LLMs often depend on external verifiers, human evaluators, or self-consistency techniques. |
| Approach: | They propose a calibrated log-likelihood-based selection framework to improve multi-LLM performance. |
| Outcome: | The proposed method outperforms majority voting and exceeds self-consistency performance when using a large number of model calls. |
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models (2025.naacl-long)
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Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya, Zora Che, Bang An, Sicheng Zhu, Aakriti Agrawal, Furong Huang
| Challenge: | PoisonedParrot is the first stealthy data poisoning attack that induces an LLM to generate copyrighted content even when the model has not been directly trained on the copyright material. |
| Approach: | They propose a stealthy data poisoning attack that induces an LLM to generate copyrighted content even when it has not been directly trained on the copyright material. |
| Outcome: | The proposed model induces an LLM to generate copyrighted content with no discernible side effects and is surprisingly effective at priming the model to generate content with little side effects. |
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings (2026.findings-acl)
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Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti, Gauri Jagatap, Jiaxin Yuan, Sarvesh Baskar, Vijay Kamarshi, Andrea Fanelli, Furong Huang
| Challenge: | Hallucinations in Large Vision-Language Models (LVLMs) are a persistent challenge, stemming from inadequate integration of visual information during multimodal reasoning. |
| Approach: | They propose a visual feature incorporation method that encourages the model to learn visually-informed textual embeddings distinct from those of the base LLM and promotes a more balanced attention distribution. |
| Outcome: | The proposed method significantly reduces hallucinations and fosters more balanced multimodal reasoning. |
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles (2026.findings-acl)
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Aakriti Agrawal, Mucong Ding, Chenghao Deng, Zora Che, Arjun Rajaram, Anirudh Satheesh, Bang An, C. Bayan Bruss, John Langford, Furong Huang
| Challenge: | Large Language Models (LLMs) are rapidly approaching and potentially exceeding human-level performance . a novel method aims to improve weak experts' generalization abilities by training them on limited human- level data . |
| Approach: | They propose a method that iteratively combines multiple weak experts to improve their generalization performance by training on limited human-level data. |
| Outcome: | The proposed method improves weak experts' generalization abilities by iterating on weak models and stronger student models. |