Unified Serialization and Prompting

• Unified serialization and prompting are systematic approaches that standardize LLM input data formats (serialization) and construct adaptive, context-aware prompts (prompting) for solving complex tasks.
• Applying this approach, like the SEAR framework for temporal table reasoning, enables LLMs to select and orchestrate different reasoning steps adaptively, integrating textual logic and code as needed.
• Empirical results show that combining unified data serialization with adaptive unified prompting significantly improves LLM performance and robustness on diverse temporal table reasoning datasets compared to fixed methods.

Unified serialization and prompting refers to the systematic strategies and representations used to standardize both the formatting of input data (serialization) and the construction of prompts (prompting) for LLMs when solving complex tasks. In the context of temporal table reasoning—a domain where questions require reasoning over sequential and structured tabular data—recent research demonstrates that no single fixed prompting technique suffices across all table and context types. Instead, robust solutions emerge from methods that unify data representation and apply adaptive, context-sensitive prompting that orchestrates varied reasoning tools.

1. Adaptive Prompting Strategies for Temporal Table Reasoning

A range of prompting techniques have been systematically benchmarked for temporal table question answering (TTQA). These include:

• Chain-of-Thought (COT): Encourages the LLM to perform stepwise intermediate reasoning, decomposing a question into logical steps.
• Evidence Extraction (EE): Directs the model to explicitly extract key evidence before answering, reducing hallucinated facts.
• Decomposition (Decomp): Breaks complex queries into modular sub-questions, each handled independently.
• Faithful Chain-of-Thought (F-COT): Maintains consistency with the initial prompt throughout the reasoning process.
• Program of Thought (POT): Guides the model to produce structured logic and ready-to-execute code snippets for advanced numerical reasoning.
• Self-Discover, Self-Ask, Plan & Solve: Modular, multi-step approaches that let the model sequentially build a reasoning plan, subdivide the problem, and then execute an answer.

Results indicate that none of these baseline methods is universally optimal: effectiveness varies with entity type, question complexity, table structure, and required reasoning modality (symbolic vs. textual). This suggests a need for adaptive strategies that select and combine the best features of each method depending on the task and data.

2. The SEAR Framework: Structured, Adaptive Reasoning

The "SEAR" (Select-Elaborate-Answer Reasoning) framework is introduced as an adaptive, structured approach for TTQA. Drawing inspiration from human problem-solving, SEAR operates as follows:

1. Select: Identify necessary high-level reasoning steps based on both table features and the posed question (e.g., whether to extract explicit evidence, decompose the query, or invoke code-based logic).
2. Elaborate: For each selected reasoning step, specify operational details (such as which columns to use or the sequence of computations), ensuring the reasoning path is explicit and logically sound.
3. Answer: Sequentially execute the reasoning plan, integrating both textual logic and symbolic/code-based operations as needed to assemble the final answer.

A "unified prompt" version, termed SEAR_UNIFIED, combines all these phases in a single, dynamic meta-prompt. This enables the LLM to autonomously select, elaborate, and carry out the appropriate sequence of reasoning steps, adapting its strategy to the characteristics of both the table and the query. The SEAR_UNIFIED prompt incorporates: (1) context and evidence extraction, (2) dynamic decomposition if required, (3) explicit switching between symbolic and textual reasoning, (4) code generation and validation, and (5) consistency checks.

Algorithmic pseudocode for SEAR_UNIFIED can be summarized as:

Given: Table T, Question Q 1. Analyze context to identify reasoning sub-goals S = {s1, s2, ...} 2. For each s in S: - Elaborate s into explicit tasks (e.g., "extract from column X", "calculate difference Y") 3. Sequentially execute each elaborated task, with code as needed 4. Aggregate results; check consistency and assemble final answer

3. Empirical Performance and Error Analysis

Comprehensive evaluation across eight diverse temporal QA datasets—featuring flat, hierarchical, and hybrid tables, as well as both textual and symbolic reasoning—shows that SEAR_UNIFIED outperforms all baseline prompting strategies according to the Hybrid Correctness Score (HCS). Results indicate:

• SEAR_UNIFIED consistently achieves the highest HCS for Gemini 1.5 Flash on all datasets, and is best or tied for GPT-4o-mini and Llama 3.1 70B on 6 out of 8 datasets.
• For example, SEAR_UNIFIED achieves up to 92.78% on TatQA, outperforming the best baseline.
• Error analysis reveals particular gains in tasks requiring robust evidence extraction, combinatorial reasoning, and in scenarios mixing symbolic and textual logic.

A plausible implication is that adaptive, unified prompting is not only more accurate but also more robust to variations in table and question complexity, minimizing reasoning failures due to method-task mismatch.

4. The Role of Unified Data Representation

Unified serialization of tabular data—standardizing tables across formats and inconsistencies—is an essential precursor to effective prompting. Real-world tables often have missing headers, multi-level structures, mixed text and numeric data, and presentation differences (CSV, Markdown, JSON). The paper uses LLM-powered prompts to refactor and serialize these tables into a canonical Markdown format: clarifying headers, harmonizing format, and adding explicit titles to resolve ambiguities.

Key findings:

• Unified table refactoring preserves almost all AutoQA accuracy (e.g., FeTaQA: 99.41%), with minimal loss of information.
• Case analyses show substantial performance boosts on refactored datasets; e.g., Squall improves by 9.69% with table standardization.
• Uniform representation allows prompting methods (including SEAR_UNIFIED) to operate unambiguously, reducing failure from input diversity.

This suggests that input standardization is foundational to enabling prompt generalization and robust performance.

5. Unified Serialization and Adaptive Prompting: Implications and Significance

The combination of unified serialization (data representation) and unified, adaptive prompting is essential for generalizable LLM-based temporal table reasoning:

• Unified serialization reduces variance in LLM input, enabling stable operation of downstream prompt-based reasoning frameworks.
• SEAR_UNIFIED demonstrates that adaptive prompts—those which internally select reasoning steps, elaborate explicit operations, and integrate both symbolic and textual logic—generalize better than any single fixed prompting baseline.
• The synergy between clean data serialization and meta-prompts that orchestrate diverse reasoning tools establishes a model-agnostic and domain-agnostic solution pipeline.
• Tasks with highly variable data heterogeneity and reasoning requirements (e.g., financial, scientific, or medical data tables) especially benefit from this two-level standardization and adaptivity.

A plausible implication is that this paired approach represents a blueprint for future agentic or hybrid neural-symbolic LLM systems in complex, knowledge-rich QA environments.

6. Conclusion and Future Prospects

Unified serialization and prompting, as instantiated by table refactoring and the SEAR_UNIFIED adaptive meta-prompt, enable LLMs to achieve robust, high-performance temporal table reasoning. No universal, static prompt exists for all scenarios; rather, adaptivity—both in input format and in prompting logic—is crucial for generality and reliability. The methods and empirical results detailed in this research provide a foundation and roadmap for the development of scalable, agent-like LLM architectures in domains requiring intricate, context-sensitive reasoning over heterogeneous data.

---

Table 1. Empirical Performance of Prompting Methods (Sample)

Dataset	Best Baseline	SEAR_UNIFIED	Gain
MultiHierTT	PoT (61.1%)	61.75%	+0.65%
HiTab	EE (80.82%)	82.61%	+1.79%
TatQA	EE (92.20%)	92.78%	+0.58%
Squall	Plan&Sol (77.0%)	81.52%	+4.52%

---

In sum, unified serialization of inputs and unified, adaptive prompting enable generalizable, accurate, and domain-transferrable temporal table reasoning with LLMs. Empirical and methodological evidence points towards their necessity as complementary pillars in future LLM-based QA systems.