Papers by Ruwan Wickramarachchi
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting (2025.coling-main)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Reddy Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, Amit Sheth
| Challenge: | Proportional analogies are used to assess linguistic and cognitive abilities. |
| Approach: | They propose a dataset for proportional analogy completion and evaluate its performance in large-scale learning environments. |
| Outcome: | The proposed model achieves 55% accuracy in knowledge-enhanced prompts. |
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models (2024.findings-eacl)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Analogies facilitate the transfer of meaning and knowledge from one domain to another. |
| Approach: | They propose to use large language models to encode syntactic and semantic structures of sentences to identify sentence analogies. |
| Outcome: | The LLMs which capture syntactic structures better, also have higher abilities in identifying sentence analogies. |
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models (2023.findings-acl)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal Gajera, Shreeyash Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das
| Challenge: | Modern large language models are evaluated on extrinsic measures based on benchmarks such as GLUE and SuperGLUE. |
| Approach: | They propose a benchmark to intrinsically evaluate large language models across a taxonomy of analogies of long text with six levels of complexity. |
| Outcome: | The proposed benchmark evaluates LLMs across a taxonomy of analogies of long text with six levels of complexity. |