Navigating the Impact of Structured Output Format on Large Language Models through the Compass of Causal Inference

• URL: http://arxiv.org/abs/2509.21791v1
• Date: Fri, 26 Sep 2025 02:47:11 GMT
• Title: Navigating the Impact of Structured Output Format on Large Language Models through the Compass of Causal Inference
• Authors: Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma,
• Abstract summary: Structured output from large language models (LLMs) has enhanced efficiency in processing generated information.<n>Some suggest that structured format enhances completeness and factual accuracy.<n>Others argue that it restricts the reasoning capacity of LLMs and leads to reductions in standard evaluation metrics.
• Score: 14.064159965912387
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Structured output from large language models (LLMs) has enhanced efficiency in processing generated information and is increasingly adopted in industrial applications. Prior studies have investigated the impact of structured output on LLMs' generation quality, often presenting one-way findings. Some suggest that structured format enhances completeness and factual accuracy, while others argue that it restricts the reasoning capacity of LLMs and leads to reductions in standard evaluation metrics. Potential limitations of these assessments include restricted testing scenarios, weakly controlled comparative settings, and reliance on coarse metrics. In this work, we present a refined analysis using causal inference. Based on one assumed and two guaranteed constraints, we derive five potential causal structures characterizing the influence of structured output on LLMs' generation: (1) collider without m-bias, (2) collider with m-bias, (3) single cause from instruction, (4) single cause from output format, and (5) independence. Across seven public and one developed reasoning tasks, we find that coarse metrics report positive, negative, or neutral effects of structured output on GPT-4o's generation. However, causal inference reveals no causal impact in 43 out of 48 scenarios. In the remaining 5, 3 involve multifaceted causal structures influenced by concrete instructions.

Related papers

• Compressed Causal Reasoning: Quantization and GraphRAG Effects on Interventional and Counterfactual Accuracy [0.0]
  This study systematically evaluate quantization effects across all three levels of Pearls Causal Ladder.<n>We find that rung level accuracy in Llama 3 8B remains broadly stable under quantization, with NF4 showing less than one percent overall degradation.<n>Experiments on the CRASS benchmark show near identical performance across precisions, indicating that existing commonsense counterfactual datasets lack the structural sensitivity needed to reveal quantization induced reasoning drift.
  arXiv  Detail & Related papers  (2025-12-13T17:54:15Z)
• Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond [64.88201012057822]
  Existing research indicates that the output of Chain-of-Thought (CoT) is significantly affected by input perturbations.<n>We theoretically analyze the effect of input perturbations on the fluctuation of CoT outputs.
  arXiv  Detail & Related papers  (2025-09-25T15:04:31Z)
• Revealing Multimodal Causality with Large Language Models [80.95511545591107]
  We propose MLLM-CD, a novel framework for multimodal causal discovery from unstructured data.<n>It consists of three key components: (1) a novel contrastive factor discovery module to identify genuine multimodal factors; (2) a statistical causal structure discovery module to infer causal relationships among discovered factors; and (3) an iterative multimodal counterfactual reasoning module to refine the discovery outcomes.<n>Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLLM-CD.
  arXiv  Detail & Related papers  (2025-09-22T13:45:17Z)
• Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks [0.7988085110283119]
  Recent results from the Corr2Cause dataset benchmark reveal that state-of-the-art LLMs only marginally outperform random baselines.<n>We provide the model with the capability to structure its thinking by guiding the model to build a structured knowledge graph.<n> Experiments on the test subset of the Corr2Cause dataset benchmark with Qwen3-32B model (reasoning model) show substantial gains over standard direct prompting methods.
  arXiv  Detail & Related papers  (2025-05-23T15:37:40Z)
• Can adversarial attacks by large language models be attributed? [1.2289361708127877]
  We show that certain classes of Large Language Models (LLMs) are not identifiable from their outputs alone.<n>We quantify the explosion in the number of plausible model origins for a given output in recent years.
  arXiv  Detail & Related papers  (2024-11-12T18:28:57Z)
• Counterfactual Causal Inference in Natural Language with Large Language Models [9.153187514369849]
  We propose an end-to-end causal structure discovery and causal inference method from natural language. We first use an LLM to extract the instantiated causal variables from text data and build a causal graph. We then conduct counterfactual inference on the estimated graph.
  arXiv  Detail & Related papers  (2024-10-08T21:53:07Z)
• Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models [59.970391602080205]
  This study investigates whether such constraints on generation space impact LLMs abilities, including reasoning and domain knowledge comprehension. We evaluate LLMs performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. We find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.
  arXiv  Detail & Related papers  (2024-08-05T13:08:24Z)
• Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference [24.565253576049024]
  This study explores the use of three state-of-the-art Large Language Models (LLMs) for crash severity inference. We generate textual narratives from original traffic crash data using a pre-built template infused with domain knowledge. We incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity.
  arXiv  Detail & Related papers  (2024-08-04T17:14:10Z)
• From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks? [51.42906577386907]
  This study explores the factors influencing the performance of Large Language Models (LLMs) in causal discovery tasks. A higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities.
  arXiv  Detail & Related papers  (2024-07-29T01:45:05Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks.<n>In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention.<n>We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types.<n>These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Self-Discover: Large Language Models Self-Compose Reasoning Structures [136.48389510481758]
  We introduce SELF-DISCOVER, a framework for self-discovering task-intrinsic reasoning structures. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks. We show that the self-discovered reasoning structures are universally applicable across model families.
  arXiv  Detail & Related papers  (2024-02-06T01:13:53Z)
• LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model [7.052058110182703]
  Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics. We explore the possibility of fine-tuning an open-sourced LLM into LLM4Causal, which can identify the causal task, execute a corresponding function, and interpret its numerical results based on users' queries and the provided dataset.
  arXiv  Detail & Related papers  (2023-12-28T16:59:06Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Inducing Causal Structure for Abstractive Text Summarization [76.1000380429553]
  We introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
  arXiv  Detail & Related papers  (2023-08-24T16:06:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.