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SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation

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arxiv 2406.14991 v2 pith:3G6DGG77 submitted 2024-06-21 cs.CL cs.SE

classification cs.CLcs.SE
keywords spreadsheetevaluationspreadsheetbenchbenchmarkchallengingfilesforumsllms
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users. Unlike existing benchmarks that rely on synthesized queries and simplified spreadsheet files, SpreadsheetBench is built from 912 real questions gathered from online Excel forums, which reflect the intricate needs of users. The associated spreadsheets from the forums contain a variety of tabular data such as multiple tables, non-standard relational tables, and abundant non-textual elements. Furthermore, we propose a more reliable evaluation metric akin to online judge platforms, where multiple spreadsheet files are created as test cases for each instruction, ensuring the evaluation of robust solutions capable of handling spreadsheets with varying values. Our comprehensive evaluation of various LLMs under both single-round and multi-round inference settings reveals a substantial gap between the state-of-the-art (SOTA) models and human performance, highlighting the benchmark's difficulty.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    SkillTTA synthesizes temporary task-specific skills from retrieved training trajectories to boost LLM agent Pass@1 scores on SpreadsheetBench and BigCodeBench without parameter updates.

  2. Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Persistent agent skill evolution is sparse, validation-filtered search whose gains depend strongly on model, benchmark, and which feedback (failures versus successes) is shown.

  3. AgentOmnia: Scaling Agentic Models for Full-Scenario Applications

    cs.AI 2026-07 conditional novelty 5.0 of 10

    Post-training an open Qwen3-30B agent on 52,361 verifiable tasks in 5,018 synthesized stateful environments lifts its average across four agent benchmarks from 22.9% to 41.7%.

  4. Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains

    cs.CL 2026-07 conditional novelty 5.0 of 10

    Relay-Bench, a 30-problem benchmark of chained multi-domain tasks with encoded prompts, resists saturation: the best tested model, GPT-5.5, scores 43.3% Pass@1.

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