Papers by Christina Lioma
Fact Checking with Insufficient Evidence (2022.tacl-1)
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| Challenge: | Existing work on how to automate fact checking relies on information obtained from external sources. |
| Approach: | They propose a fluency-preserving method for omitting information from the evidence at the constituent and sentence level and a diagnostic dataset for FC with omitted evidence. |
| Outcome: | The proposed method improves evidence sufficiency prediction by 17.8 F1 score and 2.6 F1 scores. |
DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models (2024.findings-emnlp)
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| Challenge: | LMs are useful in a variety of downstream applications from summarization to fact-checking, often relying on factual knowledge memorized during pre-training. |
| Approach: | They use two knowledge conflict measures and a novel dataset DYNAMICQA to examine the effect of intra-memory conflict on LMs' ability to accept contextual knowledge. |
| Outcome: | The proposed model can accept contextual knowledge with a higher degree of accuracy than models with fewer truth values. |
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)
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Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
| Challenge: | Existing efforts to verify factual claims are limited by small datasets or artificially constructed datasets. |
| Approach: | They propose to use the largest publicly available dataset of naturally occurring factual claims for automatic claim verification. |
| Outcome: | The proposed model outperforms baseline models and evidence pages significantly. |
A Diagnostic Study of Explainability Techniques for Text Classification (2020.emnlp-main)
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| Challenge: | Existing explainability techniques that can be produced post-hoc with already trained models are lacking a definitive guide on how to choose one given a particular task and model architecture. |
| Approach: | They propose to use a list of diagnostic properties to evaluate existing explainability techniques to compare them with human annotations of salient input regions. |
| Outcome: | The proposed list compares a set of explainability techniques on downstream text classification tasks and neural network architectures. |
A Reality Check on Context Utilisation for Retrieval-Augmented Generation (2025.acl-long)
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Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, Arnav Arora, Christina Lioma, Maria Maistro, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing studies on LM context utilisation of retrieved information have focused on synthetic text. |
| Approach: | They propose a dataset of unreliable, insufficient and difficult-to-understand contexts with real-world queries and contexts manually annotated for stance to compare them to synthetic datasets. |
| Outcome: | The proposed model outperforms synthetic datasets and exaggerates rare context characteristics, leading to inflated context utilisation results. |
How Context Shapes Truth: Geometric Transformations of Statement-level Truth Representations in LLMs (2026.acl-long)
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| Challenge: | Prior work shows that large language models encode whether a statement is true as a vector in residual stream activations. |
| Approach: | They study how truth vectors change when context is introduced in Large Language Models . they measure directional change between truth vector with and without context and relative magnitude of truth vector upon adding context. |
| Outcome: | The results show that large models distinguish relevant from irrelevant context mainly through directional change () |
Investigating the Impact of Model Instability on Explanations and Uncertainty (2024.findings-acl)
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| Challenge: | Explainable AI methods are typically evaluated holistically, but small perturbations to inputs can vastly distort explanations. |
| Approach: | They artificially simulate epistemic uncertainty in text input by introducing noise at inference time and measure the effect on the output of pre-trained language models. |
| Outcome: | The proposed model can detect salient tokens when uncertain, but it is not reliable when small perturbations are exposed during training. |
Generating Fact Checking Explanations (2020.acl-main)
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| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
| Outcome: | The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately. |
As easy as PIE: understanding when pruning causes language models to disagree (2025.findings-naacl)
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| Challenge: | Language Model pruning reduces the model's efficiency by removing weights, nodes, or other parts of its architecture. |
| Approach: | They propose to prune Language Models (LMs) to produce smaller, hence more efficient models with small loss to their effectiveness. |
| Outcome: | The proposed pruning method hurts data points that matter the most when pruning . the proposed pruning technique is based on a new study of NLP datasets . |
Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding (2022.naacl-main)
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| Challenge: | Desire is a primitive instinct and a need for strongly expressing human desires to get or possess something. |
| Approach: | They propose to use MSED to model and understand human desire . they propose to provide a benchmark for human desire analysis . |
| Outcome: | The proposed dataset contains 9,190 text-image pairs with English text. |
Faithfulness Tests for Natural Language Explanations (2023.acl-short)
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Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
| Challenge: | Existing methods for explaining neural models are misleading as they often present reasons that are unfaithful to the model’s inner workings. |
| Approach: | They propose a counterfactual input editor for inserting reasons that lead to counterfacts but are not reflected by the NLEs. |
| Outcome: | The proposed model can evaluate emerging NLE models, proving a fundamental tool in the development of faithful explanations. |