Papers with nuanced understanding of
Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering (2024.findings-acl)
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| Challenge: | Large Language Models excel in natural language understanding, but their capability for complex mathematical reasoning with a hybrid of structured tables and unstructured text remain uncertain. |
| Approach: | They propose a prompting technique tailored to semi-structured documents that matches or outperforms baselines performance while providing a nuanced understanding of LLMs' abilities. |
| Outcome: | The proposed prompting technique outperforms baseline prompting techniques while providing a nuanced understanding of LLMs' abilities. |
Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to measure word segmentation only assess the language model's understanding of the overall meaning of sentences, lacking an evaluation of the language models' understanding capabilities at a fine-grained level. |
| Approach: | They propose a framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) they employ current mainstream LLMs to perform word segmentations across multiple languages . |
| Outcome: | The proposed method improves on existing methods and combines the advanced pattern recognition capabilities of Aho-Corasick automata with the deep insights of well-pretrained LLMs. |