Papers by Zara Siddique
Dialz: A Python Toolkit for Steering Vectors (2025.acl-demo)
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| Challenge: | *Dialz* is a Python library for advancing research on steering vectors for open-source LMs. |
| Approach: | They propose a Python library for advancing research on steering vectors for open-source LMs. |
| Outcome: | The proposed method reduces harmful outputs and provides insights into model behaviour across different layers. |
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs (2026.findings-eacl)
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| Challenge: | Despite efforts to mitigate social bias in large language models, representational harms such as stereotyping continue to exist in both open and closed-source models. |
| Approach: | They propose a method to modify model activations in forward passes by applying steering vectors to a BBQ dataset and comparing their results to bias mitigation methods. |
| Outcome: | The proposed method outperforms 3 other bias mitigation methods on the BBQ dataset and shows the lowest impact on MMLU scores. |
Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models (2024.emnlp-main)
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| Challenge: | Existing research on stereotypes in large language models is limited and focuses on African Ameri- F. |
| Approach: | They propose to use global bias to probe a set of large language models via perplexity to determine how certain stereotypes are represented in the model's internal representations. |
| Outcome: | The proposed model amplifys harmful stereotypes and shows that the demographic groups associated with stereotypes remain consistent across model likelihoods and outputs. |
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
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Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
| Outcome: | The proposed models can extract metaphors from literary texts without using domain experts. |