feat: shorten abstract, change the description about benchmark corpus

[AdrianM0]

• Removed Figure 3 temporarily
• Replaced part of the description that was based on embeddings
• Shortened the abstract

Summary by Sourcery

Shorten the abstract and refine the benchmark corpus description by removing redundant details and providing a more concise overview of the topics and classifications.

Enhancements:

• Condense the abstract by removing redundant information and streamlining the description of LLM capabilities and their evaluation framework.
• Refine the description of the benchmark corpus by summarizing the range of topics and question types, and introducing classifications based on techniques and difficulty.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request shortens and refines the abstract of a scientific paper about evaluating large language models (LLMs) in the context of chemical sciences. The changes focus on making the abstract more concise while maintaining the key points of the research. The modifications include removing some detailed explanations, rephrasing sentences for brevity, and slightly adjusting some numerical values.

File-Level Changes

Files	Changes
src/tex/ms.tex	Condensed the introduction of LLMs and their relevance to chemical sciences
src/tex/ms.tex	Simplified the description of the ChemBench framework
src/tex/ms.tex	Updated the number of question-answer pairs from 7,000 to 7,500
src/tex/ms.tex	Removed detailed explanations about the distribution of topics and question diversity
src/tex/ms.tex	Added a brief description of question types and classification
src/tex/ms.tex	Removed a figure and its associated description (question_diversity.pdf)

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[sourcery-ai]

Hey @AdrianM0 - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider reincorporating the paragraph about adaptations to chemistry curricula and the importance of developing evaluation frameworks, as it provides valuable implications of the research.
• While the abstract is now more concise, consider retaining one of the figures (e.g., topic distribution or question diversity) to visually reinforce the breadth of the study.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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