Papers by Tatiana Anikina
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem (2025.coling-main)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
| Approach: | They propose a role-modeling approach that employs two LLMs as generator and critic to generate and refine NLEs. |
| Outcome: | The proposed model outperforms self-refine and can perform with less powerful LLMs. |
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems (2024.findings-emnlp)
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| Challenge: | Existing systems based on large language models (LLMs) are more precise and reliable in identifying users’ intentions, but the recognition of intents still presents a challenge in the case of ConvXAI, since little training data exist and the domain is highly specific. |
| Approach: | They propose to use a dataset in the NLP domain for user intent recognition in ConvXAI to improve parsing performance. |
| Outcome: | The proposed system outperforms existing methods and improves on existing ones. |
A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
| Approach: | They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs. |
| Outcome: | The proposed generation strategies and their combinations yield strong results across 11 languages, including several extremely low-resource ones. |
To Clarify or not to Clarify: A Comparative Analysis of Clarification Classification with Fine-Tuning, Prompt Tuning, and Prompt Engineering (2024.naacl-srw)
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| Challenge: | Xu et al., 2019) show that pre-trained language model fine-tuning and prompt tuning are better than manual prompt engineering for clarification identification. |
| Approach: | They propose to use pre-trained language model fine-tuning, prompt tuning and manual prompt engineering to model clarification identification. |
| Outcome: | The proposed model outperforms pre-trained language model fine-tuning, prompt tuning and manual prompt engineering on the task of clarification identification. |
Towards Efficient Dialogue Processing in the Emergency Response Domain (2023.acl-srw)
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| Challenge: | Adapters perform dialogue act classification and domain-specific slot tagging in the emergency response domain. |
| Approach: | They propose to build a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust. |
| Outcome: | The proposed model performs well in the emergency response domain while being efficient, flexible and robust. |
Large Language Models for Multilingual Previously Fact-Checked Claim Detection (2025.findings-emnlp)
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| Challenge: | a new study evaluates large language models for multilingual previously fact-checked claim detection . authors assess seven LLMs across 20 languages in monolingual and cross-lingual settings . |
| Approach: | They evaluate large language models for multilingual previously fact-checked claim detection . they find they perform well for high-resource languages, struggle with low-resourced languages . |
| Outcome: | The proposed model performs well for high-resource languages, but struggle with low-resourced languages. |
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. |
Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems (2025.findings-emnlp)
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Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Fedor Splitt, Jiaao Li, Yoana Tsoneva, Sebastian Möller, Vera Schmitt
| Challenge: | Current ConvXAI systems are based on intent recognition to accurately identify the user’s desired intention and map it to an explainability method. |
| Approach: | They propose a multilingual extension of the CoXQL dataset spanning five typologically diverse languages, including one low-resource language. |
| Outcome: | The proposed model enables multilingual generalization in a multilingual dataset spanning five typologically diverse languages, including one low-resource language. |
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations (2023.findings-emnlp)
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| Challenge: | Recent work on NLP explainability methods lacks a dialogue-based interpretability framework that can convey faithful explanations in human-understandable terms. |
| Approach: | They adapt the conversational explanation framework TalkToModel to the NLP domain and add new NLP-specific operations such as free-text rationalization to illustrate its generalizability. |
| Outcome: | The proposed framework can be used to explain models on three NLP tasks and is generalizable to different datasets, use cases and models. |