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    <abstract>Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first systematic comparison of instruction optimization, based on the DSPy optimization framework, for tabular fact verification. We evaluate four out-of-the-box prompting techniques that cover both text-only prompting and code use: direct prediction, Chain-of-Thought (CoT), ReAct with SQL tools, and CodeAct with Python execution. We study three optimizers from the DSPy framework—COPRO, MiPROv2, and SIMBA—across four benchmarks and three model families. We find that instruction optimization consistently improves verification accuracy, with MiPROv2 yielding the most stable gains for CoT, and SIMBA providing the largest benefits for ReAct agents, particularly at larger model scales. Behavioral analyses reveal that SIMBA encourages more direct reasoning paths by applying heuristics, thereby improving numerical comparison abilities in CoT reasoning and helping avoid unnecessary tool calls in ReAct agents. Across different prompting techniques, CoT remains effective for tabular fact checking, especially with smaller models. Although ReAct agents built with larger models can achieve competitive performance, they require careful instruction optimization.</abstract>
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%0 Conference Proceedings
%T Analyzing LLM Instruction Optimization for Tabular Fact Verification
%A Du, Xiaotang
%A Hong, Giwon
%A Kwan, Wai-Chung
%A Saxena, Rohit
%A Titov, Ivan
%A Minervini, Pasquale
%A Allaway, Emily
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F du-etal-2026-analyzing
%X Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first systematic comparison of instruction optimization, based on the DSPy optimization framework, for tabular fact verification. We evaluate four out-of-the-box prompting techniques that cover both text-only prompting and code use: direct prediction, Chain-of-Thought (CoT), ReAct with SQL tools, and CodeAct with Python execution. We study three optimizers from the DSPy framework—COPRO, MiPROv2, and SIMBA—across four benchmarks and three model families. We find that instruction optimization consistently improves verification accuracy, with MiPROv2 yielding the most stable gains for CoT, and SIMBA providing the largest benefits for ReAct agents, particularly at larger model scales. Behavioral analyses reveal that SIMBA encourages more direct reasoning paths by applying heuristics, thereby improving numerical comparison abilities in CoT reasoning and helping avoid unnecessary tool calls in ReAct agents. Across different prompting techniques, CoT remains effective for tabular fact checking, especially with smaller models. Although ReAct agents built with larger models can achieve competitive performance, they require careful instruction optimization.
%U https://aclanthology.org/2026.findings-eacl.161/
%P 3078-3108

Markdown (Informal)

[Analyzing LLM Instruction Optimization for Tabular Fact Verification](https://aclanthology.org/2026.findings-eacl.161/) (Du et al., Findings 2026)

• Analyzing LLM Instruction Optimization for Tabular Fact Verification (Du et al., Findings 2026)

ACL

• Xiaotang Du, Giwon Hong, Wai-Chung Kwan, Rohit Saxena, Ivan Titov, Pasquale Minervini, and Emily Allaway. 2026. Analyzing LLM Instruction Optimization for Tabular Fact Verification. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3078–3108, Rabat, Morocco. Association for Computational Linguistics.