Reasoning Beyond the Obvious: Evaluating Divergent and Convergent Thinking in LLMs for Financial Scenarios

• URL: http://arxiv.org/abs/2507.18368v1
• Date: Thu, 24 Jul 2025 12:47:29 GMT
• Title: Reasoning Beyond the Obvious: Evaluating Divergent and Convergent Thinking in LLMs for Financial Scenarios
• Authors: Zhuang Qiang Bok, Watson Wei Khong Chua,
• Abstract summary: ConDiFi is a benchmark that jointly evaluates divergent and convergent thinking in LLMs for financial tasks.<n> GPT-4o underperforms on Novelty and Actionability, while models like DeepSeek-R1 and Cohere Command R+ rank among the top for generating actionable, insights suitable for investment decisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most reasoning benchmarks for LLMs emphasize factual accuracy or step-by-step logic. In finance, however, professionals must not only converge on optimal decisions but also generate creative, plausible futures under uncertainty. We introduce ConDiFi, a benchmark that jointly evaluates divergent and convergent thinking in LLMs for financial tasks. ConDiFi features 607 macro-financial prompts for divergent reasoning and 990 multi-hop adversarial MCQs for convergent reasoning. Using this benchmark, we evaluated 14 leading models and uncovered striking differences. Despite high fluency, GPT-4o underperforms on Novelty and Actionability. In contrast, models like DeepSeek-R1 and Cohere Command R+ rank among the top for generating actionable, insights suitable for investment decisions. ConDiFi provides a new perspective to assess reasoning capabilities essential to safe and strategic deployment of LLMs in finance.

Related papers

• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [55.328782443604986]
  Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data.<n>This paper offers the first quantitative analysis of confirmation bias in LLM-based investment analysis.<n>We observe a consistent preference for large-cap stocks and contrarian strategies across most models.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis [1.3812010983144802]
  We evaluate how various large language models (LLMs) align with human-labeled sentiment in a financial context.<n>Our findings suggest that reasoning, either through prompting or inherent model design, does not improve performance on this task.<n>Surprisingly, the most accurate and human-aligned combination of model and method was GPT-4o without any Chain-of-Thought (CoT) prompting.
  arXiv  Detail & Related papers  (2025-06-05T02:47:23Z)
• JudgeLRM: Large Reasoning Models as a Judge [65.14085339820795]
  We investigate whether Large Language Models (LLMs) judges truly benefit from enhanced reasoning capabilities.<n>We introduce JudgeLRM, a family of judgment-oriented LLMs trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards.
  arXiv  Detail & Related papers  (2025-03-31T02:18:51Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)
• Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving [55.895917967408586]
  Existing approaches to mathematical reasoning with large language models rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation.<n>We propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously.
  arXiv  Detail & Related papers  (2025-02-17T16:56:23Z)
• Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance [35.617409883103335]
  FinReason is the first financial reasoning benchmark covering multi-table analysis, long-context reasoning, and equation-based tasks.<n>We introduce FinCoT, the first open high-fidelity CoT corpus for finance, distilled from seven QA datasets.<n>We develop Fin-o1, the first open financial reasoning models trained via supervised fine-tuning and GRPO-based RL.
  arXiv  Detail & Related papers  (2025-02-12T05:13:04Z)
• Chat Bankman-Fried: an Exploration of LLM Alignment in Finance [4.892013668424246]
  As jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured.<n>This paper proposes an experimental framework to assess whether large language models (LLMs) adhere to ethical and legal standards in the relatively unexplored context of finance.
  arXiv  Detail & Related papers  (2024-11-01T08:56:17Z)
• DeFine: Decision-Making with Analogical Reasoning over Factor Profiles [35.9909472797192]
  textscDeFine is a modular framework that constructs probabilistic factor profiles from complex scenarios.<n>It then integrates these profiles with analogical reasoning to guide LLMs in making critical decisions in new situations.<n>This approach is particularly useful in areas such as consulting and financial deliberation, where making decisions under uncertainty is vital.
  arXiv  Detail & Related papers  (2024-10-02T17:29:34Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• Sentiment Analysis through LLM Negotiations [58.67939611291001]
  A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round. This paper introduces a multi-LLM negotiation framework for sentiment analysis.
  arXiv  Detail & Related papers  (2023-11-03T12:35:29Z)
• Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models [53.620827459684094]
  Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks. We propose the first open-source comprehensive framework for exploring LLMs for credit scoring. We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
  arXiv  Detail & Related papers  (2023-10-01T03:50:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.