ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2210.03849v1)

Abstract: With the recent advance in large pre-trained LLMs, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.

An Insightful Overview of "ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering"

The paper "ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering" addresses a significant challenge in the application of LLMs to financial question answering, specifically involving complex numerical reasoning. This work introduces a new dataset, ConvFinQA, which aims to foster advancements in understanding and modeling numerical reasoning within financial dialogues.

Motivation and Context

Current research in NLP has made significant progress with the advent of large pre-trained LLMs (LMs), yet these models primarily excel at language pattern matching tasks. Complex reasoning tasks, particularly those requiring a chain of numerical reasoning — similar to human cognitive processing — still present formidable challenges. The financial domain, characterized by its reliance on intricate numerical analysis and quantitative evaluations, provides an apt testbed for exploring these challenges.

Dataset and Methodology

ConvFinQA is constructed to examine conversational question answering (ConvQA) scenarios over financial documents. The dataset comprises 3,892 conversations and 14,115 questions derived from financial reports, simulating real-world financial analysis where iterative questions are asked to gain insights into numerical data. The dataset's construction involved the decomposition of multi-hop reasoning problems and the recreation of conversations to reflect natural dialog flow in financial inquiry.

The authors compare neural symbolic methods and prompting-based approaches to understand their effectiveness in capturing complex reasoning patterns. Neural symbolic models, like FinQANet, leverage structured reasoning programs to decode the logic required to derive answers. In contrast, prompting-based models, such as those using the GPT-3 architecture, attempt to initiate reasoning through carefully curated prompts.

Key Findings

1. Performance Challenges: Both neural symbolic and prompting-based models show considerable gaps compared to human expert performance, with execution accuracy below 70%. ConvFinQA necessitates reasoning over dependencies formed across conversation turns, posing challenges in distinguishing when to use prior context or adopt new information.
2. Prompting Limitations: While GPT-3 is capable of executing simple numerical tasks by leveraging pre-trained knowledge, its performance deteriorates when confronted with novel reasoning paradigms related to financial tasks, indicating potential constraints in its generalization abilities.
3. Domain-specific Knowledge: The domain-specific nature of the financial questions highlighted the limitations of large LMs that have not been explicitly pre-trained within specialized domains, emphasizing a potential need for domain-adaptive fine-tuning.

Implications and Future Directions

The ConvFinQA dataset serves as an important step towards developing models that can handle real-world, numerically intensive reasoning tasks. The performance gaps identified suggest that while pre-trained LMs demonstrate general understanding, their application-specific capabilities require enhancement, particularly in domains like finance. Future work may explore integrating domain-specific knowledge into existing LMs or developing hybrid models that enrich linguistic understanding with symbolic reasoning capabilities.

The results also raise critical inquiries about the boundaries of task paradigms amenable to LLMs and how to bridge these for complex reasoning tasks. Understanding and overcoming these challenges is imperative as AI continues to move towards more nuanced, decision-support roles in professional domains such as finance.

Conclusion

The ConvFinQA project underscores the importance of advancing NLP research beyond language pattern recognition towards incorporating robust reasoning mechanisms. By creating a challenging financial dataset and analyzing current methodologies, this paper provides a pathway for future research focused on enhancing AI’s capacity to emulate human-like, complex reasoning in real-world settings.