We present the system developed by the Central China Normal University (CCNU) team for the SemEval-2025 shared task 8, which focuses on Question-Answering (QA) for tabular data. Our approach leverages multiple Large Language Models (LLMs), conducting tabular QA as code completion. Additionally, to improve its reliability, we introduce a two-stage corrections mechanism, in which we instruct the LLM to correct the code according to the judges of whether the code is executable and whether the answer obtained from executing the code is semantically consistent with the question. The experiment demonstrates that code correction works but answer correction does not. Finally, we discuss other unsuccessful approaches explored during our development process.

Remarkable progress has been made on automated reasoning with natural text, by using Large Language Models (LLMs) and methods such as Chain-of-Thought prompting and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from intended conclusion to supporting axioms) is significantly more efficient at proof-finding. Importing this intuition into the LM setting, we develop a Backward Chaining algorithm, called LAMBADA, that decomposes reasoning into four sub-modules, that are simply implemented by few-shot prompted LLM inference. We show that LAMBADA achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.