Papers by Sayna Ebrahimi
TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are difficult to explain and understand due to long input contexts and autoregressive output generation. |
| Approach: | They propose a post-hoc explanation method which incorporates LLM-specific techniques. |
| Outcome: | The proposed method improves retrieval recall and prediction accuracy significantly on open-domain question answering benchmarks. |
Reverse Thinking Makes LLMs Stronger Reasoners (2025.naacl-long)
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Justin Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
| Challenge: | Reverse-Enhanced Thinking (RevThink) is a framework for large language models to perform reverse thinking. |
| Approach: | They propose a framework for enhancing forward-backward reasoning by collecting data from a teacher model and employing three objectives to train a student model in a multi-task learning fashion. |
| Outcome: | The proposed framework outperforms a fine-tuning method trained on 10x more forward reasoning on 12 datasets covering commonsense, math, and logical reasoning. |
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive capabilities in many tasks, including natural language understanding and generation. |
| Approach: | They propose a framework for adaptation with self-evaluation to improve selective prediction performance of large language models. |
| Outcome: | The proposed framework outperforms state-of-the-art selective prediction methods on QA datasets and improves the AUACC from 91.23% to 92.63% and AUROC from 74.61% to 80.25%. |