Papers by Viktor Schlegel
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering (2024.findings-acl)
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Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler
| Challenge: | Existing studies on adapting large language models to perform a variety of tasks in high-stakes domains such as healthcare lack understanding of the extent and contributing factors that allow them to recall relevant knowledge and combine it with presented information. |
| Approach: | They propose to use multiple choice and abstractive question answering to investigate the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain. |
| Outcome: | The proposed models perform better on 22 datasets in three generalist and three specialist biomedical sub-domains, and show that they can generalise to unseen sub- domains. |
Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World (2026.findings-acl)
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| Challenge: | Current robustness evaluation methods rely on static synthetic perturbations to stress-test models. |
| Approach: | They propose a framework for automatically evaluating QA models under naturally occurring textual perturbations by replacing context passages with revised Wikipedia edit histories. |
| Outcome: | The proposed framework replaces context passages with revised Wikipedia edit histories to improve model performance. |
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the CHATGPT Era and Beyond (2024.eacl-long)
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Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria
| Challenge: | Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. |
| Approach: | They present a systematic review of the literature on sentence representations focusing mostly on deep learning models. |
| Outcome: | The proposed methods highlight the key contributions and challenges in this area and suggest potential avenues for improving the quality and efficiency of sentence representations. |
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings (D19-53)
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| Challenge: | DBee provides a data model which operates over knowledge graphs and embedding vector spaces . |
| Approach: | They describe a database which provides a data model which exploits the semantic properties of large-scale knowledge graphs and embedding vector spaces. |
| Outcome: | The proposed model exploits the semantic properties of both types of representations. |
Natural Context Drift Undermines the Natural Language Understanding of Large Language Models (2025.findings-emnlp)
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| Challenge: | generative Large Language Models (LLMs) are based on natural text evolution . |
| Approach: | They propose a framework for curating naturally evolved variants of reading passages from contemporary QA benchmarks and for analysing LLM performance across a range of semantic similarity scores. |
| Outcome: | The proposed framework evaluates QA datasets and LLMs with publicly available training data. |
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)
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| Challenge: | Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community. |
| Approach: | They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. |
| Outcome: | The proposed model outperforms existing approaches on signs unobserved during training. |
Incorporating Zoning Information into Argument Mining from Biomedical Literature (2022.lrec-1)
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| Challenge: | Argumentative zoning is a text zonation scheme that is used to segment text into zones that serve distinct functions. |
| Approach: | They propose to use zoning information to incorporate into argument mining tasks . they add zonation labels predicted by an off-the-shelf model to the beginning of each sentence . |
| Outcome: | The proposed models improve argument mining models without additional annotation cost. |
A Two-Stage Decoder for Efficient ICD Coding (2023.findings-acl)
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| Challenge: | Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. |
| Approach: | They propose a two-stage decoding mechanism to predict ICD codes using hierarchical properties of the codes to split the prediction into two steps: at first, predict the parent code and then predict the child code based on the previous prediction. |
| Outcome: | Experiments on the public MIMIC-III data show that the proposed model performs well in single-model settings without external data or knowledge. |
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation (2023.acl-long)
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| Challenge: | Argument summarisation is a promising but currently under-explored field. |
| Approach: | They propose a framework to generate key points from short texts in a task known as Key Point Analysis. |
| Outcome: | The proposed framework improves state-of-the-art in argument summarisation with performance improvement of 14 percentage points compared to ROUGE and human evaluation scores. |
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
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| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
| Approach: | They propose to evaluate the explainability of rationales in ICD coding using a multi-granular rationale-annotated dataset. |
| Outcome: | The proposed model improves the explainability of rationales in ICD coding by using human-annotated rationale-announced rationale models. |
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)
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| Challenge: | Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards. |
| Approach: | They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards. |
| Outcome: | The proposed framework investigates the present linguistic features, required reasoning and background knowledge and factual correctness on the one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
Can Transformers Reason in Fragments of Natural Language? (2022.emnlp-main)
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| Challenge: | Recent work on natural language inference has identified two strands of research . |
| Approach: | They investigate whether neural networks have acquired logical principles from natural language . they use transformer-based models to detect valid inferences in controlled fragments of natural language. |
| Outcome: | The proposed model overfits to superficial patterns in the data rather than acquiring the logical principles governing reasoning in natural language fragments. |
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour (2022.lrec-1)
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| Challenge: | Forced labour is the most common type of modern slavery, affecting at least 24.9 million people worldwide. |
| Approach: | They propose to annotate an English corpus for multi-class and multi-label forced labour detection using specialised data from specialised sources. |
| Outcome: | The proposed corpus consists of 989 news articles annotated according to risk indicators defined by the International Labour Organization (ILO). |
uMedSum: A Unified Framework for Clinical Abstractive Summarization (2025.acl-long)
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Aishik Nagar, Yutong Liu, Andy T. Liu, Viktor Schlegel, Vijay Prakash Dwivedi, Arun-Kumar Kaliya-Perumal, Guna Pratheep Kalanchiam, Yili Tang, Robby T. Tan
| Challenge: | Clinical abstractive summarization struggles to balance faithfulness and informativeness, sacrificing key information or introducing confabulations. |
| Approach: | They develop a modular hybrid framework that integrates confabulation removal and key information addition into abstractive summarization methods. |
| Outcome: | The proposed framework outperforms state-of-the-art abstractive summarization methods in both quantitative metrics and expert evaluations. |
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models (2022.lrec-1)
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| Challenge: | Existing studies on NLP touch upon moral contexts in text. |
| Approach: | They construct a dataset that can be used for moral judgement tasks on a popular reddit subreddit. |
| Outcome: | The proposed model passes moral judgements on posts from a popular reddit subreddit . it shows that the model can be fine tuned and improves across the datasets . |
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers? (2024.emnlp-main)
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| Challenge: | State-of-the-art Large Language Models (LLMs) are accredited with a number of different capabilities, including reading comprehension, mathematical and reasoning skills, and possessing scientific knowledge. |
| Approach: | They propose a benchmark to generate seemingly plausible multi-hop reasoning chains that ultimately lead to incorrect answers. |
| Outcome: | The proposed model circumvents the reasoning requirement but in subtle ways . it shows that it is more difficult to generate plausible alternatives . |
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks (D19-53)
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| Challenge: | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. |
| Approach: | They propose a document-structured message passing architecture for the identification of supporting facts over a graph-structure based representation of text. |
| Outcome: | The proposed model outperforms a baseline reading comprehension test on raw text and shows that it is relevant for multi-hop reasoning. |
Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement (2026.acl-long)
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| Challenge: | Existing approaches to argument summarization rely on single-pass generation, offering limited support for factual correction or structural refinement. |
| Approach: | They propose a large language diffusion framework that iteratively improves argument summarization by sufficiency-guided remasking and regeneration. |
| Outcome: | Empirical results show that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 evaluation metrics. |
Argument mining as a multi-hop generative machine reading comprehension task (2023.findings-emnlp)
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| Challenge: | Argument mining is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. |
| Approach: | They propose a new approach which transfers the argument mining task into a multi-hop reading comprehension task by incorporating a "chain of thought" information into the model. |
| Outcome: | The proposed approach surpasses SOTA results on two arguments mining benchmarks. |
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension? (2021.eacl-main)
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| Challenge: | Existing benchmarks for machine reading comprehension (MRC) are insufficient to assess models for their capabilities to read and comprehend . |
| Approach: | They propose an ablation-based method to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations. |
| Outcome: | The proposed method shows that the model's performance drops on three large-scale datasets . the results suggest that most of the answers do not require understanding the discourse structure of the text. |