Papers by Mahmud Elahi Akhter
Does Transliteration Help Multilingual Language Modeling? (2023.findings-eacl)
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| Challenge: | Script diversity presents a challenge to Multilingual Language Models by reducing lexical overlap . Script diversification can be used to improve performance of MLLMs by transliterating closely related languages to a common script. |
| Approach: | They empirically measure the effect of transliteration on MLLMs by focusing on Indic languages . they find that transliterations benefit low-resource languages without negatively affecting high-resourced ones . |
| Outcome: | The proposed transliteration-based model learns sentences that are more similar across languages. |
Temporal reasoning for timeline summarisation in social media (2025.acl-long)
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| Challenge: | Existing temporal reasoning datasets focus on pair-wise event relationships. |
| Approach: | They propose a temporal reasoning dataset focused on temporal relationships among sequential events within narratives that combines temporal thinking with timeline summarisation through a knowledge distillation framework. |
| Outcome: | The proposed model achieves superior performance on mental health-related timeline summarisation tasks, highlighting the importance and generalisability of leveraging temporal reasoning to improve timeline summaries. |
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models (2026.findings-acl)
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Mingzi Cao, Xingwei Tan, Mahmud Elahi Akhter, Marco Valentino, Maria Liakata, Xi Wang, Nikolaos Aletras
| Challenge: | Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. |
| Approach: | They propose to use a dataset of symbolic tasks to induce deductive skills into large language models (LLMs) they then use FT to fine-tune models to improve OOD generalization . |
| Outcome: | The proposed approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic out-of-domain tasks. |
Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification (2025.findings-acl)
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John Dougrez-Lewis, Mahmud Elahi Akhter, Federico Ruggeri, Sebastian Löbbers, Yulan He, Maria Liakata
| Challenge: | Large Language Models (LLMs) have shown remarkable proficiency in complex tasks where reasoning capabilities are paramount. |
| Approach: | They propose a framework to break down claims into atomic reasoning types needed for verification. |
| Outcome: | The proposed framework breaks down claims into atomic reasoning types needed for verification. |
Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision (2025.emnlp-main)
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| Challenge: | Large language models have shown strong performance in many reasoning benchmarks, but lack robust planning or symbolic abstractions. |
| Approach: | They propose to synthesize high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. |
| Outcome: | The proposed method can be trained on high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale using Monte Carlo estimation. |