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Md Mahadi Hasan Nahid

2 accepted papers

2024

NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization

EMNLP 2024finding

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in parsing textual data and generating code. However, their performance in tasks involving tabular data, especially those requiring symbolic reasoning, faces challenges due to the structural variance and inconsis…

2024

TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition

NAACL 2024long

Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data. Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation, but they often struggle with large tables due to their limi…