Papers by Katja Filippova
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data (2020.findings-emnlp)
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| Challenge: | Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. |
| Approach: | They propose a technique to treat hallucinations as a controllable aspect of the generated text without dismissing any input and without modifying the model architecture. |
| Outcome: | The proposed technique can be used on a WikiBio dataset and in a human evaluation. |
“Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (2022.emnlp-main)
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| Challenge: | Existing work on faithfulness evaluation is not conclusive and does not provide a clear answer as to how different methods are to be compared. |
| Approach: | They propose a protocol for faithfulness evaluation that makes use of partially synthetic data to obtain ground truth for feature importance ranking. |
| Outcome: | The proposed method is based on partially synthetic data and is compared with lexical shortcuts on a range of datasets and LSTM models. |
We Need To Talk About Random Splits (2021.eacl-main)
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| Challenge: | argued that random splits, like standard splits lead to overly optimistic performance estimates. |
| Approach: | They argue that random splits, like standard splits lead to overly optimistic performance estimates. |
| Outcome: | The proposed method leads to more realistic performance estimates than standard splits. |
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets (2023.emnlp-main)
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| Challenge: | Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP, however, data quality might have already become the bottleneck to unlock further gains. |
| Approach: | They propose a general method for improving model performance in the presence of noisy training data based on self-influence and bandit curriculum learning. |
| Outcome: | The proposed method improves model performance in machine translation, question answering and text classification, building up on approaches to self-influence calculation and automated curriculum learning. |
Dissecting Recall of Factual Associations in Auto-Regressive Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have focused on identifying where factual knowledge is encoded in the network, but little is known about how it is extracted from the model parameters during inference. |
| Approach: | They examine how factual associations are stored and retrieved internally in LMs . they use attention edges to identify critical points where information propagates to the prediction . |
| Outcome: | The proposed model aggregates information about subject and relation to predict the correct attribute . the model “queries” the enriched subject to extract the attribute based on the proposed model . |
Language Models Struggle to Use Representations Learned In-Context (2026.acl-long)
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| Challenge: | a recent study shows that large language models are capable of inducing rich representations of data that are seen in-context . a novel task, adaptive world modeling, shows that even the most performant LLMs cannot reliably leverage novel semantics defined in-constitut. |
| Approach: | They propose to use in-context representations to induce rich representations of data . they also propose to probe models using a novel task to enable flexible deployment . |
| Outcome: | The proposed model can use in-context representations to complete simple downstream tasks. |
Controlling Machine Translation for Multiple Attributes with Additive Interventions (2021.emnlp-main)
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| Challenge: | A standard approach for exerting control in MT is to prepend the input with a special tag to signal the desired output attribute. |
| Approach: | They propose a vector-valued approach which allows for fine-grained control over multiple attributes simultaneously via a weighted linear combination of the corresponding vectors. |
| Outcome: | The proposed approach achieves better control over a wider range of tasks than tagging and even fine-tuning a model trained without annotations. |