MacroBench: A Novel Testbed for Web Automation Scripts via Large Language Models

We introduce MacroBench, a code-first benchmark that evaluates whether LLMs can synthesize reusable browser automation programs from natural language goals by reading HTML/DOM and emitting Python with Selenium. MacroBench instantiates seven self-hosted sites: Airbnb-like, TikTok-like, Reddit-like, Instagram-like, Facebook-like, Discord-like, and Threads-like, covering 681 tasks across interaction complexity and targeting difficulty. Our end-to-end protocol validates generated code via static checks, sandboxed execution, and outcome verification including DOM assertions and database snapshots, and includes a safety suite for scraping, spam/abuse, and credential/privacy prompts. Across 2636 model-task runs, we observe stratified success: GPT-4o-Mini achieves 96.8 percent, GPT-4.1 achieves 95.3 percent, Gemini-2.5-Pro achieves 89.0 percent, and DeepSeek-V3.1 achieves 83.4 percent. Models handle simple tasks reliably at 91.7 percent but fail on complex workflows at 0.0 percent, and none meet production-quality coding practices despite functional completion. We release our complete benchmark pipeline, evaluation framework, and experimental results to enable reproducible assessment of macro synthesis for web automation.

Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation (RAG) has emerged as a promising framework to mitigate hallucinations in Large Language Models (LLMs), yet its overall performance is dependent on the underlying retrieval system. In the finance domain, documents such as 10-K reports pose distinct challenges due to domain-specific vocabulary and multi-hierarchical tabular data. In this work, we introduce an efficient, end-to-end RAG pipeline that enhances retrieval for financial documents through a three-phase approach: pre-retrieval, retrieval, and post-retrieval. In the pre-retrieval phase, various query and corpus preprocessing techniques are employed to enrich input data. During the retrieval phase, we fine-tuned state-of-the-art (SOTA) embedding models with domain-specific knowledge and implemented a hybrid retrieval strategy that combines dense and sparse representations. Finally, the post-retrieval phase leverages Direct Preference Optimization (DPO) training and document selection methods to further refine the results. Evaluations on seven financial question answering datasets-FinDER, FinQABench, FinanceBench, TATQA, FinQA, ConvFinQA, and MultiHiertt-demonstrate substantial improvements in retrieval performance, leading to more accurate and contextually appropriate generation. These findings highlight the critical role of tailored retrieval techniques in advancing the effectiveness of RAG systems for financial applications. A fully replicable pipeline is available on GitHub: https://github.com/seohyunwoo-0407/GAR.