Papers by Barry-John Theobald
Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models (2025.acl-long)
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Anirudh Sundar, Sinead Williamson, Katherine Metcalf, Barry-John Theobald, Skyler Seto, Masha Fedzechkina
| Challenge: | Large language models (LLMs) exhibit impressive performance on a variety of tasks from text summarization to zero-shot common-sense reasoning. |
| Approach: | They propose to manipulate the embedding space of mLLMs by manipulating its activations to steer generation into the desired direction. |
| Outcome: | The proposed model interventions improves alignment of cross-lingual representations in multilingual large language models with up to 2x improvements in top-1 accuracy on cross-linguistic retrieval tasks. |
Bias after Prompting: Persistent Discrimination in Large Language Models (2025.findings-emnlp)
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Nivedha Sivakumar, Natalie Mackraz, Samira Khorshidi, Krishna Patel, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff
| Challenge: | a dangerous assumption is that biases do not transfer from pre-trained large language models to adapted models. |
| Approach: | They validate the bias transfer hypothesis by using prompt adaptations to study biases in causal models . they find that popular prompt-based mitigation methods do not consistently prevent biase transferring . |
| Outcome: | The results invalidate the assumption that biases do not transfer from pre-trained models to adapted models. |
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization (2024.findings-emnlp)
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Yong Lin, Skyler Seto, Maartje Ter Hoeve, Katherine Metcalf, Barry-John Theobald, Xuan Wang, Yizhe Zhang, Chen Huang, Tong Zhang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. |
| Approach: | They compare the accuracy of DPORM and EXRM with a reward function for scoring human preferences. |
| Outcome: | The proposed methods can approximate an EXRM on the limit infinite samples, but it is unclear how effective they are in practice. |