# GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning

arXiv: https://arxiv.org/abs/2404.02934 · HTML: https://mlsanity.com/paper/2404.02934/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 19 of 19)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://mlsanity.com/paper/2401.01335/) (2024)
- [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models](https://mlsanity.com/paper/2310.02949/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [Evaluating the Moral Beliefs Encoded in LLMs](https://mlsanity.com/paper/2307.14324/) (2023)
- [InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning](https://mlsanity.com/paper/2309.13064/) (2023)
- [FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets](https://mlsanity.com/paper/2310.04793/) (2023)
- [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023)
- [Large Language Models for Supply Chain Optimization](https://mlsanity.com/paper/2307.03875/) (2023)
- [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://mlsanity.com/paper/2306.12659/) (2023)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023)
- [Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges](https://mlsanity.com/paper/2308.05391/) (2023)
- [GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models](https://mlsanity.com/paper/2309.03079/) (2023)
- [Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes](https://mlsanity.com/paper/2307.07422/) (2023)
- [Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools](https://mlsanity.com/paper/2308.14182/) (2023)
- [Language Models for Business Optimisation with a Real World Case Study in Production Scheduling](https://mlsanity.com/paper/2309.13218/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026)
- [CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives](https://mlsanity.com/paper/2504.10823/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation](https://mlsanity.com/paper/2501.18177/) (2025)
- [MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment](https://mlsanity.com/paper/2603.17694/) (2026)
- [Do Emotions Influence Moral Judgment in Large Language Models?](https://mlsanity.com/paper/2604.19125/) (2026)
- [EMNLP: Educator-role Moral and Normative Large Language Models Profiling](https://mlsanity.com/paper/2508.15250/) (2025)
- [IREA: Intermediate Representation-based Embedding Alignment for Normative RAG](https://mlsanity.com/paper/2610.04974/) (2026)

## Abstract (arXiv)

This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes. By comparing GreedLlama's performance in moral reasoning tasks to a base Llama2 model, our results highlight a concerning trend: GreedLlama demonstrates a marked preference for profit over ethical considerations, making morally appropriate decisions at significantly lower rates than the base model in scenarios of both low and high moral ambiguity. In low ambiguity situations, GreedLlama's ethical decisions decreased to 54.4%, compared to the base model's 86.9%, while in high ambiguity contexts, the rate was 47.4% against the base model's 65.1%. These findings emphasize the risks of single-dimensional value alignment in LLMs, underscoring the need for integrating broader ethical values into AI development to ensure decisions are not solely driven by financial incentives. The study calls for a balanced approach to LLM deployment, advocating for the incorporation of ethical considerations in models intended for business applications, particularly in light of the absence of regulatory oversight.