Conducting a Qualitative Analysis by Comparing the Outputs of Our Think-and-Execute Framework

Written by transcompiler | Published 2025/03/25
Tech Story Tags: algorithmic-reasoning-in-lm | pseudocode-reasoning | language-model-optimization | task-level-logic | language-models | think-and-execute-framework | python-programming | think-and-execute

TLDRWe conduct a qualitative analysis by comparing the outputs of our approach (THINKAND-EXECUTE) with those of the baseline methods. via the TL;DR App

Table of Links

Abstract and 1. Introduction

2 Think-and-Execute

3 Experimental Setup

4 Results

5 Analysis

6 Related Work

7 Limitations and Discussion

8 Conclusion and References

A Experimental Details

B Details of Think-and-Execute

C Prompts Used in Our Experiments

D Human-written Pseudocode Prompts

E Generated Analyses

F Generated Pseudocode Prompts

G Qualitative Analysis

G Qualitative Analysis

We conduct a qualitative analysis by comparing the outputs of our approach (THINKAND-EXECUTE) with those of the baseline methods. This comparison is presented across Tables7,8,9,10,11,12, and 13.

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

Authors:

(1) Hyungjoo Chae, Yonsei University;

(2) Yeonghyeon Kim, Yonsei University;

(3) Seungone Kim, KAIST AI;

(4) Kai Tzu-iunn Ong, Yonsei University;

(5) Beong-woo Kwak, Yonsei University;

(6) Moohyeon Kim, Yonsei University;

(7) Seonghwan Kim, Yonsei University;

(8) Taeyoon Kwon, Yonsei University;

(9) Jiwan Chung, Yonsei University;

(10) Youngjae Yu, Yonsei University;

(11) Jinyoung Yeo, Yonsei University.


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Published by HackerNoon on 2025/03/25