Related Work
Large Language Models for Table Reasoning .
Primary approaches using LLMs to tackle table reasoning tasks involve fine-tuning a foundational model following the pre-LLM era or directly utilizing in-context learning abilities unique to the LLM era. For fine-tuning methods, following the success of mask language modeling (MLM), task-specific fine-tuning methods are designed. For example, TaPas Herzig et al. ( 2020 ) extends BERT’s Devlin et al. ( 2019 ) architecture and enhances the understanding of tabular data by recovering masked cells. Moreover, models relying on logical codes ( e.g., SQL) can further enhance the model’s reasoning ability. For example, Tapex Liu et al. ( 2022 ) and OmniTab Jiang et al. ( 2022 ) focus on generating SQL queries that are then executed to fetch relevant information.

Prompting technologies such as few-shot learning Brown et al. ( 2020 ) , chain-of-thought reasoning (COT) Wei et al. ( 2022 ) , and agent-based methods Wang et al. ( 2024a ) can be correspondingly applied in table reasoning tasks. Chen ( 2023 ) first explores and demonstrates the feasibility of using LLMs in generic reasoning tasks. Binder Cheng et al. ( 2023 ) shows symbolic languages are also beneficial for complex analysis with prompt methods. Chain-of-Table Wang et al. ( 2024b ) , inspired by CoT prompting methods, uses tabular data in the reasoning chain as a proxy for intermediate thoughts. ReAcTable Zhang et al. ( 2023 ) employs LLMs extending the ReAct framework to reason step-by-step and iteratively generates sub-tables using code executors. Dater Ye et al. ( 2023 ) and DIN-SQL Pourreza and Rafiei ( 2023 ) break down table reasoning into multi-step inference by handcrafting pipeline.

Query Augmentation .
In question-answering tasks, query augmentation or query rewrite is a prevalent method to bridge the gap between queries and facts. Within the framework of LLMs, tasks related to Retrieval-Augmented Generation (RAG) often involve various forms of query modification, including query rewriting, disambiguation, and decomposition, etc (Gao et al., 2023 ) . RQ-RAG (Chan et al., 2024 ) equips the model with multiple capabilities in multi-hop QA tasks. Ma et al. ( 2023 ) proposes Rewrite-Retrieve-Read pipeline which adapt the query itself. Step-Back Prompting Zheng et al. ( 2024 ) presents a simple technique to derive high-level concepts. Our method further supplements sampled table content to better suit the table question answering scenario.

Table Augmentation and Table sampling .
Table augmentation involves integrating knowledge and exploring implicit table content. Mainstream methods include the incorporation of commonsense knowledge from search engines (Sui et al., 2023) or analytical knowledge (He et al., 2023; Jena et al., 2022) into inference processes. Sui et al. (2024) leverages the LLM itself to augment structural information using internal knowledge. Instead, the augmentation in the table augmentor is closely aligned with our ALTER framework, which is utilized throughout the process. For table sampling, Lin et al. (2023) fine-tune DPR (Karpukhin et al., 2020) to retrieve sub-tables and TabSQLify (Nahid and Rafiei, 2024) relies on SQL queries to decompose tables into relevant sub-tables.