Papers by Raha Moraffah
Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge. |
| Approach: | They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs. |
| Outcome: | The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints. |
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts (2023.findings-emnlp)
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| Challenge: | Existing methods to detect AI-generated text are inadequate, causing misuse of the text. |
| Approach: | They propose a universal evasive prompt framework that can prompt any PLM to generate “human-like” text that can mislead detectors. |
| Outcome: | The proposed approach can prompt any PLM to generate “human-like” text that can mislead detectors. |
Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models (2024.emnlp-main)
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| Challenge: | Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases. |
| Approach: | They propose to exploit openness of RAG models by injecting deceptive content into the retrieval database, intentionally changing the model’s behavior. |
| Outcome: | The proposed model can be exploited through crafted content uploads with access to the retriever. |
Can Large Language Models Infer Causal Relationships from Real-World Text? (2026.acl-long)
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| Challenge: | Existing work evaluating large language models relies on synthetic or simplified texts with explicit causal relationships. |
| Approach: | They develop a benchmark to evaluate LLMs' ability to infer causal relationships from texts . they use a dataset of texts with different levels of explicitness and complexity . |
| Outcome: | The proposed benchmark is the first-ever real-world dataset for this task. |
Exploiting Class Probabilities for Black-box Sentence-level Attacks (2024.findings-eacl)
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| Challenge: | Existing sentences use either no feedback or only the class labels for sentence-level attacks. |
| Approach: | They propose an algorithm that uses class probabilities for black-box sentence-level attacks and investigate the effectiveness of using class probabilties on the attack’s success. |
| Outcome: | The proposed algorithm is evaluated against baselines and classifiers and compares with the existing models to determine whether it is worthy or practical to use class probabilities for black-box sentence-level attacks. |