Papers by Erik Arakelyan
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)
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| Challenge: | Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation. |
| Approach: | They propose a method for generating and validating QA datasets for low-resource languages . they use English data as context to generate synthetic multiple-choice (MC) question-answer pairs . |
| Outcome: | The proposed method maintains quality, reduces likelihood of factual errors, and circumvents costly annotation. |
FLARE: Faithful Logic-Aided Reasoning and Exploration (2025.emnlp-main)
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| Challenge: | Modern Question Answering (QA) and Reasoning approaches with Large Language Models (LLMs) use Chain-of-Thought (CoT) prompting but struggle with ambiguous tasks. |
| Approach: | They propose a method that uses large language models to plan solutions and formalize queries without external solvers to generate outputs faithful to their intermediate reasoning chains. |
| Outcome: | The proposed method achieves SOTA results on 7 out of 9 diverse reasoning benchmarks and 3 out of 3 logic inference benchmarks while enabling measurement of reasoning faithfulness. |
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection (2023.acl-long)
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| Challenge: | Stance Detection is a task that aims to identify the attitudes of an author towards a target of interest. |
| Approach: | They propose a topic-guided diversity sampling technique and a contrastive objective to improve stance detection using the produced set. |
| Outcome: | The proposed method outperforms the state-of-the-art on 16 datasets with in-domain and out-of domain evaluations and is more generalizable with an averaged 10.2 F1 on out-domain evaluation. |
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models (2024.eacl-long)
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| Challenge: | Recent studies have indicated that NLI models have an understanding of lexical and compositional semantics. |
| Approach: | They propose a framework to assess the extent of semantic sensitivity in NLI models . they use adversarially generated examples with minor semantics-preserving surface-form variations . |
| Outcome: | The proposed framework shows that NLI models struggle with minor variations requiring knowledge of compositional semantics . |