[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81549-en":3,"doc-seo-81549-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81549,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",8,"Research & Report","Decoupling Task-Solving and Output Formatting in LLM Generation","Large language models increasingly solve complex tasks, yet performance degrades when prompts entangle task instructions with rigid output-format requirements, creating competing objectives and harming reasoning. The framework DECO-G explicitly separates format adherence from problem solving by routing formatting control to a Format Estimation Module (FEM). FEM uses probabilistic lookahead to estimate future compliance and reweights token probabilities, letting the LLM focus on task resolution. Instruction-aware distillation, flexible trie construction, and HMM state pruning make the method practical.","Decoupling Task-Solving and Output Formatting in LLM Generation  \nHaikang Deng, Po-Nien Kung, Nanyun Peng  \nUniversity of California, Los Angeles {haikang, ponienkung, [violetpeng}@cs.ucla.edu](violetpeng}@cs.ucla.edu)  \narXiv :2510 .03595v2 [ cs .CL] 10 Jul 2026  \nAbstract  \nLarge language models (LLMs) are increasingly adept at solving complex problems, such as mathematical reasoning and automatic evaluation. However, performance often degrades when prompts intertwine task instructions with rigid formatting requirements. This entanglement creates competing goals for the model, hindering its reasoning capabilities. To address this, we introduce DECO-G, a decoding framework that explicitly decouples format adherence from problem solving. DECOG delegates format adherence to a separate Format Estimation Module (FEM), which performs probabilistic lookahead to estimate future format compliance rate and reweighs token probabilities, allowing the LLM to focus solely on task resolution. To make this approach both practical and efficient, we introduce three key innovations: instruction-aware distillation, a flexible trie-building algorithm, and HMM state pruning. Experiments across mathematical reasoning, event argument extraction, and LLM-as-a-judge demonstrate that DECO-G constantly gains over prompting or structured generation baselines, with guaranteed format compliance. We release our code at § haikangdeng/deco-g.  \n1 Introduction  \nInstruction fine-tuning (Wei et al., 2021 ; Chunget al., 2024) empowers LLMs to solve complex tasks, often enhanced by reasoning strategies like Chain-of-Thought (Wei et al., 2022) and Tree-ofThought (Yao et al., 2023) . However, emerging evidence suggests that combining problem-solving instructions with strict output-format requirements in a single prompt negatively impacts performance (Tam et al., 2024 ; Long et al., 2025 ; He et al., 2024) . For instance, Long et al. (2025) demonstrate that output structure significantly affects accuracy on benchmarks like MMLU (Hendryckset al., 2020), while Tam et al. (2024) observe that  \nstricter constraints correlate with greater reasoning degradation. This suggests that the current paradigm of intertwining task and format instructions (as shown in Figure 1) may hurt LLMs’ reasoning capabilities.  \nTo mitigate this, recent works have explored relaxing format strictness (Tam et al., 2024) or adopting more intuitive schema (Long et al., 2025 ; He et al., 2024) . Yet, these adjustments still impose constraints that distract LLMs from reasoning. Alternatively, constrained decoding frameworks (Beurer-Kellner et al., 2024 ; guidance-ai, 2024 ; Willard and Louf, 2023) ensure compliance by strictly enforcing token transitions. However, this rigid intervention does not consider the model’s internal reasoning flow, resulting in abrupt cut-offs or incoherent outputs. This trade-off highlights the critical need for a framework that seamlessly decouples format constraints from task solving to unlock the full potential of LLMs.  \nIn this paper, we introduce DECO-G, a framework that explicitly decouples format adherence from task reasoning, allowing LLMs to focus solely on problem-solving while introducing an auxiliary module to guarantee format adherence. Specifically, it leverages the modularity of existing controllable text generation methods (e.g. GeLaTo (Zhang et al., 2023), Ctrl-G (Zhang et al., 2024)) and delegates format adherence to a dedicated Format Estimation Module (FEM) that continuously estimates the final format compliance and reweighs each nexttoken distribution to guide generation.  \nWhile this work builds upon GeLaTo (Zhang et al., 2023) and Ctrl-G (Zhang et al., 2024), it is, to our knowledge, the first framework that introduces explicit separation of task solving and format adherence to preserve LLMs’ full potential. Moreover, naively applying existing methods leads to degraded performance, due to their limited compatibility with instruction-tuned LLMs an","cbCaicfqNVHAHUk3","https://ap.wps.com/l/cbCaicfqNVHAHUk3","pdf",921441,3,1,18,"English","en",105,"# Introduction\n# Preliminaries\n# Mathematical Reasoning\n## Format Constraints Example","[{\"question\":\"What problem does DECO-G address in LLM generation?\",\"answer\":\"DECO-G addresses degraded reasoning performance caused by entangling task instructions with strict output-format constraints in a single prompt.\"},{\"question\":\"How does DECO-G ensure output format compliance without hurting reasoning?\",\"answer\":\"DECO-G decouples formatting from task solving by delegating format adherence to a Format Estimation Module (FEM) that estimates future compliance and reweights next-token probabilities.\"},{\"question\":\"What innovations make the DECO-G approach practical and efficient?\",\"answer\":\"DECO-G introduces instruction-aware distillation, a flexible trie-building algorithm, and HMM state pruning to improve compatibility with instruction-tuned LLMs and reduce computational bottlenecks.\"}]",1784174240,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"decoupling-task-solving-and-output-formatting-in-llm-generation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/decoupling-task-solving-and-output-formatting-in-llm-generation/81549/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does DECO-G address in LLM generation?","Question",{"text":75,"@type":76},"DECO-G addresses degraded reasoning performance caused by entangling task instructions with strict output-format constraints in a single prompt.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does DECO-G ensure output format compliance without hurting reasoning?",{"text":80,"@type":76},"DECO-G decouples formatting from task solving by delegating format adherence to a Format Estimation Module (FEM) that estimates future compliance and reweights next-token probabilities.",{"name":82,"@type":73,"acceptedAnswer":83},"What innovations make the DECO-G approach practical and efficient?",{"text":84,"@type":76},"DECO-G introduces instruction-aware distillation, a flexible trie-building algorithm, and HMM state pruning to improve compatibility with instruction-tuned LLMs and reduce computational 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