GISA: A Benchmark for General Information-Seeking Assistant
#GISA #Information-Seeking Assistant #Large Language Models #Benchmark #Web Interactions #Multi-turn Queries #AI Evaluation #arXiv
📌 Key Takeaways
- GISA is a new benchmark for evaluating information-seeking agents
- Existing benchmarks create unnatural tasks by working backward from answers
- Current benchmarks focus on narrow aspects of information seeking
- GISA aims to better align evaluation with real-world information needs
📖 Full Retelling
Researchers introduced GISA, a new benchmark for evaluating general information-seeking assistants, in a paper posted on arXiv on February 8, 2026, aiming to address limitations in existing evaluation methods that create unnatural tasks misaligned with real-world information needs. The advancement of large language models has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web interactions, yet current benchmarks fail to adequately evaluate these complex systems. GISA represents a significant step forward in creating evaluation frameworks that more accurately reflect how humans actually seek information online, addressing the critical gap between academic benchmarks and practical applications. The benchmark focuses on comprehensive information-seeking scenarios rather than narrow tasks, providing a more holistic assessment of an AI assistant's capabilities in navigating the complexities of real-world information retrieval.
🏷️ Themes
Artificial Intelligence, Information Retrieval, Benchmark Development
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
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Original Source
arXiv:2602.08543v2 Announce Type: replace-cross
Abstract: The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web interactions. Various benchmarks have been proposed to evaluate such agents. However, existing benchmarks often construct queries backward from answers, producing unnatural tasks misaligned with real-world needs. Moreover, these benchmarks tend to focus on eith
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