How to handle tables during chunking

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Table of Contents

• How to handle tables during chunking

• Introduction

• Table Handling in Document Digitization

• Optimizing Retrieval for Table Data

• Processing Hierarchical Lists and Outlines

• Conclusion

Introduction

Modern document digitization pipelines for large language models (LLMs) must handle complex structures like tables and lists. Naïve text chunking often fails to account for these structures (HERE). Recent research (2024–2025) has explored robust parsing methods for tables (from PDFs, HTML, and databases) and better chunking strategies, along with optimized retrieval techniques that combine structured querying with semantic search. Below, we review key advances in table processing and list handling for LLM-based systems, highlighting how they improve comprehension and retrieval.

Table Handling in Document Digitization

Robust Table Parsing: A major challenge is extracting tables from PDFs (including scanned documents) and preserving their structure for LLM ingestion. Perez et al. (2024) propose a multi-strategy parsing pipeline that integrates traditional text extraction with an LLM-powered OCR module . This approach parses each page into nodes (e.g. Text, Table, Image nodes) and uses an LLM to directly read complex elements like tables and even describe images . By coupling standard OCR with LLM-based parsing, such systems achieve high fidelity extraction of table content (cells, rows, columns), even from noisy or image-based PDFs . The parsed tables are then represented in a structured markup (e.g. markdown or HTML) to retain their grid layout and cell relationships.

Serialization and Formats: How tables are serialized (CSV, HTML, JSON, etc.) can affect LLM understanding (Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study). Recent benchmarks evaluate different input formats – from plain text with separators to HTML/XML markup – to see which formats LLMs parse most effectively . While LLMs pretrained on web data have some ability to parse HTML tables, research indicates that adding structural cues (like Markdown pipes or explicit headers) can improve comprehension . The “Table Meets LLM” study (WSDM 2024) introduced structured prompts and found that preserving table schemas (column names, hierarchical headers) in the input leads to better cell value extraction and reasoning . In practice, pipelines often convert tables into a text format with clear row/column demarcations or use specialized tokens to indicate table structure.

Chunking Strategies for Tables: Tables often do not fit into a single context window, so chunking is required. A simple row-by-row split can break context or lose header associations. To address this, advanced ingestion frameworks create table-specific chunks augmented with context. For example, one approach is to generate a natural-language summary or description of a table and embed that instead of raw cell text (HERE). Perez et al. (2024) note that directly embedding raw table cells (especially numeric data) is suboptimal – numbers alone have weak semantic meaning – so they embed a contextualized description of the table that captures key insights or relations . This preserves the table’s meaning in vector space, aiding LLM retrieval and comprehension. Additionally, treating each table as a separate node allows linking it with its surrounding text. The ingestion agent can maintain pointers such as “Table X (with caption Y)” so the LLM knows how the table relates to the narrative. Such node-based chunking (tables, figures, text) with hierarchical relationships outperforms flat segmentation , as it keeps tables intact and connected to relevant context (e.g. section headers).

Optimizing Retrieval for Table Data

Structured & Semantic Retrieval: Once tables are indexed as chunks, effective retrieval is crucial. Traditional keyword search or pure vector search may miss relevant table data. TableRAG (Chen et al., 2024) is a retrieval-augmented generation framework tailored for tables that combines structured querying with semantic search. TableRAG uses a multi-stage retrieval: first an LLM expands the user query and retrieves relevant schema elements (e.g. column names) from a database table, then it retrieves matching cell values from those columns (TableRAG: Million-Token Table Understanding with Language Models). By performing schema retrieval followed by cell retrieval, the system pinpoints crucial rows/columns without scanning entire tables. This dramatically reduces prompt length and avoids overwhelming the LLM with irrelevant cells . In fact, TableRAG’s design makes the input context size independent of the full table size, enabling million-token tables to be handled efficiently . On large table benchmarks (ArcadeQA, BIRD-SQL), TableRAG achieved the highest retrieval precision and set new state-of-the-art performance in table question answering . This demonstrates the benefit of combining structured queries (identifying the right columns) with semantic similarity (finding relevant cells) for table-based retrieval.

Metadata and Filtering: Another line of work improves retrieval by leveraging metadata. Multi-Meta-RAG (Poliakov & Shvai, 2024) augments standard RAG pipelines with database-style filtering using metadata extracted by an LLM (HERE). For instance, in a multi-hop QA scenario, an LLM first reads the query and infers structured metadata (e.g. the topic domain or source type requested). The retrieval system then filters the knowledge base by those metadata fields before semantic search . This hybrid of structured filtering and embedding search yields significant gains in precision. In experiments on a news QA benchmark, integrating LLM-derived metadata improved top-4 retrieval hit rate by over 17% . In practice, this means retrieved chunks are far more likely to come from the correct source or timeframe, reducing irrelevant results. Such structured querying can be seen as a lightweight “database query” that narrows the candidate pool for the vector retriever. The combination of the two yields better relevance and is more explainable (one can inspect the metadata used) . Similarly, Zhong et al. (2024) propose a Mix-of-Granularity router that dynamically chooses chunk size (fine-grained or coarse) based on the query, ensuring tables or structured data are searched at the right granularity (Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation). These approaches all aim to balance semantic embedding retrieval with structure-aware querying to handle tabular data effectively.

Database Tables and Text-to-SQL: When dealing with live database tables (as opposed to static text), LLMs can also generate SQL queries – but identifying the correct tables/columns is non-trivial. Retrieval can assist here as well. RB-SQL (Wu et al., 2024) is a framework that retrieves concise schema snippets and relevant examples to help an LLM answer database questions (RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL). It selects the most relevant tables and columns from a large schema and provides them as context, along with past solved queries, for in-context learning. This structured retrieval dramatically simplifies the prompt, so the LLM doesn’t get distracted by unrelated tables. RB-SQL showed improved accuracy on complex benchmarks (e.g. Spider) by focusing the LLM on just the needed sub-tables . In essence, even for databases, combining a structural lookup (finding which table or field is needed) with the LLM’s reasoning improves performance. Together, these retrieval optimizations ensure that table-based information is accessed in a targeted, efficient manner, rather than treating tables as flat blobs of text.

Processing Hierarchical Lists and Outlines

Lists – whether bulleted, numbered, or multi-level – carry intrinsic structure (order, hierarchy) that LLMs should understand. Simply concatenating list items into a paragraph can lose this structure. Recent methods therefore preserve lists as distinct structured elements during chunking. For example, Perez et al.’s node-based parser explicitly captures titled lists, ordered lists, and bullet point lists as separate text nodes, each “requiring specific consideration” (HERE). Rather than splitting a list into arbitrary chunks, the entire list or sublist is kept together in the extracted markdown, with indentation or numbering intact. This way, an LLM sees the list in its natural form (e.g. “1., 2., 3.” or “• item A, • item B”) and can infer relationships like sequence or nesting.

Some key techniques for list handling include:

• Maintaining Hierarchy: Nested list items are represented with indentation or parent-child links, often mimicking HTML or Markdown structure. This hierarchical encoding lets the model know, for example, that sub-bullets are under a parent bullet. In one approach, the ingestion system established parent-child node relationships (header → subheader → list item) so that each list item node knows its context . Preserving these links helps the LLM retrieve a list item with its context (e.g. the section it belongs to).

• Metadata in Lists: Lists often contain embedded metadata – e.g. a list of definitions (Term: Description), or a list of steps with timestamps. Systems handle this by parsing each list item’s internal structure. An item might be split into a key–value pair in the embedding (for example, store the “term” as a field and the “description” as its content), enabling more structured querying. If a user asks about a specific term, the retriever can match it to the list item’s key. This strategy of augmenting list items with metadata tags can improve semantic search when list entries have repetitive formats.

• Contextual Chunking of Lists: Deciding chunk boundaries for long lists is tricky – too short and items lose context, too long and the chunk overflows. A practical strategy is to chunk along logical breaks: e.g. one chunk per top-level list, or grouping 5–10 bullet points per chunk if a list is very long. Each chunk carries an identifier of which part of the list it is (e.g. “Items 1–5 of X”) to maintain order. Researchers have noted that LLMs can follow list ordering if the numbering is present (HERE), so retaining numbering in each chunk helps the model reconstruct the full list or step sequence. Additionally, instructing the LLM (during retrieval-augmented generation) to present answers as a list can leverage its strength in enumerating points clearly .

By handling lists as first-class structured data, these methods improve LLM comprehension. The model can better understand that a series of bullet points are related and sequential, rather than independent sentences. Hierarchical lists (like outlines or multi-level FAQs) especially benefit: preserving their tree structure in the indexed text means an LLM can answer queries that reference an entire list or a specific sub-point. Overall, treating lists with appropriate structure and metadata yields more faithful and context-aware responses from LLMs.

Conclusion

In summary, the latest research underscores that structure-aware processing is key to digitizing documents for LLM consumption. For tables, this means accurate parsing (sometimes with LLM assistance for OCR), intelligent chunking that retains table context (headers, row relations), and hybrid retrieval that uses both schema-level queries and semantic embeddings (TableRAG: Million-Token Table Understanding with Language Models). For lists, it means preserving sequence and hierarchy so that LLMs grasp the ordered information. By combining these techniques with retrieval-augmented generation, modern systems can significantly boost answer relevancy and factuality when dealing with tabular or listed information (HERE) . As context windows expand, such structured chunking and retrieval optimizations will remain crucial for aligning LLMs with the rich, complex formats of real-world documents.

References: Selected from arXiv preprints (2024–2025).