Papers by Golnoosh Farnadi
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset (2025.naacl-long)
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| Challenge: | Learning from human feedback (LHF) has been used to mitigate the harms of large language models (LLMs) but the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. |
| Approach: | They audit the Helpful and Harmless (HH) dataset by Anthropic and examine how conceptualization failures and quality issues identified in the dataset can create additional harms . |
| Outcome: | The findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in large language models. |
REVIVING YOUR MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing (2025.emnlp-main)
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| Challenge: | Existing evaluation methods assess performance after LLMs are fine-tuned or unlearned to adapt to new tasks or eliminate undesirable behaviors. |
| Approach: | They propose a framework for identifying unintended side effects using sparse model diffing. |
| Outcome: | The proposed framework can detect unintended side effects without fine-tuning data . it achieves 95% accuracy in predicting side effects, aligning with known benchmarks . |
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs (2026.eacl-long)
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Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi
| Challenge: | Existing studies on unlearning in multilingual large language models focus on monolingual settings, typically English. |
| Approach: | They propose to use a multilingual data and concept unlearning model to investigate the problem . they extend benchmarks for factual knowledge and stereotypes into ten languages . |
| Outcome: | The proposed model is able to unlearning in 10 languages across five languages and resource levels. |
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework (2026.eacl-long)
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Clea Chataigner, Rebecca Ma, Prakhar Ganesh, Yuhao Chen, Afaf Taik, Elliot Creager, Golnoosh Farnadi
| Challenge: | Existing studies have studied prompt sensitivity by altering formatting or generating paraphrases with automated techniques. |
| Approach: | They propose a framework for generating controlled paraphrases grounded in user behaviors . they leverage linguistically informed rules and enforce quality through checks on instruction adherence . |
| Outcome: | The proposed framework is able to detect weaknesses in large language models . it leverages linguistically informed rules and enforces quality through checks on instruction adherence, semantic similarity, and realism. |
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training (2025.acl-long)
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| Challenge: | Existing studies have focused on identifying and addressing hallucinations in large language models (LLMs), but the impact of the training process on hallucinosity remains underexplored. |
| Approach: | They propose a training protocol to reduce hallucination variance by dropping embedding indices with significant variability and an unsupervised halluciation detection metric, Efficient EigenScore. |
| Outcome: | The proposed training protocol reduces hallucination variance during training by dropping embedding indices with significant variability. |
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards (2024.findings-acl)
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Khaoula Chehbouni, Megha Roshan, Emmanuel Ma, Futian Wei, Afaf Taik, Jackie Cheung, Golnoosh Farnadi
| Challenge: | Recent advances in large language models have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. |
| Approach: | They propose to use LLMs to evaluate their safety responses on already mitigated biases by evaluating models on already encoded assumptions. |
| Outcome: | The proposed model can encode harmful assumptions, but it can also be harmful for certain demographic groups. |
Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity (2026.eacl-long)
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| Challenge: | Existing hallucination evaluations focus only on correctness and often overlook consistency . a significant inconsistency in benchmarks like Med-HALT suggests hallucianation-related harms have been misunderstood. |
| Approach: | They propose a framework for quantifying consistency in hallucination evaluations . they find that detection techniques detect consistency, not correctness . |
| Outcome: | The proposed framework uncovers critical limitations in hallucination evaluations. |