Knowledge‑Based Neural‑Symbolic Discrete Reasoning: OPERA, UniRPG‑2, and Large‑Model Inference

Overview This talk, titled “Knowledge‑Based Neural & Symbolic Integrated Discrete Reasoning,” is organized into four main parts: (1) lightweight‑operator‑centric text discrete reasoning (OPERA), (2) knowledge‑grounded unified discrete reasoning via program generation (UniRPG‑2), (3) the current status of zero‑ and few‑shot reasoning with large models, and (4) a summary with future outlook.

1. Lightweight‑Operator Text Discrete Reasoning (OPERA) The OPERA model (NAACL 2022) tackles reading‑comprehension tasks that require logical operations such as arithmetic, comparison, and sorting. It predicts a set of operators from the input text and question, then executes them through a two‑stage architecture: an encoder for context, a lightweight operator selector, an operation executor, and a predictor. Experiments on the DROP dataset show significant improvements over prior methods.

2. Knowledge‑Grounded Unified Discrete Reasoning (UniRPG‑2) To handle structured and heterogeneous data, the UniRPG‑2 framework extends the earlier UniRPG (EMNLP 2022) by generating executable programs grounded in external knowledge. It supports single‑turn (e.g., TAT‑QA) and multi‑turn (e.g., PACIFIC) QA, offering stronger interpretability through program generation. The architecture includes a structure‑aware knowledge reader, a mask‑aware encoder, and a decoder that uses pointer‑attention and a prediction vocabulary to emit operators and assemble a program.

3. Zero‑Shot & Few‑Shot Reasoning with Large Models Large language models (e.g., ChatGPT) can parse natural‑language questions into logical forms and even generate code such as Python quick‑sort implementations. Compared with smaller supervised models, large models require no external retriever or extensive annotation, relying on internal knowledge. Techniques like Chain‑of‑Thought (CoT) prompting and Program‑of‑Thought (PoT) further enhance reasoning performance. Empirical results on DROP, TAT‑QA, and PACIFIC demonstrate good accuracy and clear attention heatmaps.

4. Summary and Outlook Future research directions include: (1) building trustworthy large models by integrating external knowledge graphs to mitigate factual errors, (2) ensuring safe, controllable decoding under legal and ethical constraints, and (3) exploring interdisciplinary insights from neuroscience and biology to understand the origins of model reasoning abilities.