Papers by Jenny Kunz
A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Models (2024.eacl-srw)
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| Challenge: | Recent advances in LLMs generating longer coherent text have popularised self-rationalising models, which produce a natural language explanation alongside their output. |
| Approach: | They propose a Bayesian network-based hypothesis-driven statistical framework that allows us to judge how similar LLM-generated free-text explanations are to LLMs. |
| Outcome: | The proposed framework does not exhibit a strong similarity to GPT-3.5. |
Classifier Probes May Just Learn from Linear Context Features (2020.coling-main)
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| Challenge: | Current probing methods can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations. |
| Approach: | They propose to use token embeddings to test whether probing tasks contain linguistic structure . they argue that current probing methods do not provide enough information to support this hypothesis . |
| Outcome: | The proposed method can be scrutinized and proves that representations encode linguistic structure even without additional linguistic structures. |
Where Does Linguistic Information Emerge in Neural Language Models? Measuring Gains and Contributions across Layers (2022.coling-1)
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| Challenge: | Probing studies have explored where in neural language models linguistic information is located . standard approach is to focus on the layers whose representations give the highest performance on probing tasks . |
| Approach: | They propose a method that asks where task-relevant information emerges in the model by focusing on the layers that give the highest performance. |
| Outcome: | The proposed method confirms the expected ordering only for one of the pairs, indicating that the features that contribute the most to probing tasks are not as high-level as global metrics suggest. |
Only for the Unseen Languages, Say the Llamas: On the Efficacy of Language Adapters for Cross-lingual Transfer in English-centric LLMs (2025.acl-srw)
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| Challenge: | Most state-of-the-art large language models (LLMs) are trained mainly on English data, limiting their effectiveness on non-English, especially low-resource, languages. |
| Approach: | They train language adapters for 13 languages and evaluate their effectiveness on downstream tasks using either task adapters or in-context learning. |
| Outcome: | The proposed language adapters improve performance for languages not seen during pretraining, but provide negligible benefit for seen languages. |