Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches

Recent progress in large language models (LLMs) has outpaced the development of effective evaluation methods. Traditional benchmarks rely on task-specific metrics and static datasets, which often suffer from fairness issues, limited scalability, and contamination risks. In this paper, we introduce Teach2Eval, an indirect evaluation framework inspired by the Feynman Technique. Instead of directly testing LLMs on predefined tasks, our method evaluates a model's multiple abilities to teach weaker student models to perform tasks effectively. By converting open-ended tasks into standardized multiple-choice questions (MCQs) through teacher-generated feedback, Teach2Eval enables scalable, automated, and multi-dimensional assessment. Our approach not only avoids data leakage and memorization but also captures a broad range of cognitive abilities that are orthogonal to current benchmarks. Experimental results across 26 leading LLMs show strong alignment with existing human and model-based dynamic rankings, while offering additional interpretability for training guidance.

SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning

Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these domains. However, each field encompasses a variety of specific tasks that require learning, and the diverse, heterogeneous data across these domains can lead to conflicts during model task transfer. In response to this challenge, our study introduces an Adaptive Semantic Space Learning (ASSL) framework, which utilizes the adaptive reorganization of data distributions within the semantic space to enhance the performance and selection efficacy of multi-expert models. Utilizing this framework, we trained a financial multi-task LLM named "SilverSight". Our research findings demonstrate that our framework can achieve results close to those obtained with full data training using only 10% of the data, while also exhibiting strong generalization capabilities.

Are Large Language Models Rational Investors?

Large Language Models (LLMs) are progressively being adopted in financial analysis to harness their extensive knowledge base for interpreting complex market data and trends. However, their application in the financial domain is challenged by intrinsic biases (i.e., risk-preference bias) and a superficial grasp of market intricacies, underscoring the need for a thorough assessment of their financial insight. This study introduces a novel framework, Financial Bias Indicators (FBI), to critically evaluate the financial rationality of LLMs, focusing on their ability to discern and navigate the subtleties of financial information and to identify any irrational biases that might skew market analysis. Our research adopts an innovative methodology to measure financial rationality, integrating principles of behavioral finance to scrutinize the biases and decision-making patterns of LLMs. We conduct a comprehensive evaluation of 19 leading LLMs, considering factors such as model scale, training datasets, input strategies, etc. The findings reveal varying degrees of financial irrationality among the models, influenced by their design and training. Models trained specifically on financial datasets might exhibit greater irrationality, and it's possible that even larger financial language models (FinLLMs) could display more biases than smaller, more generalized models. This outcomes provide profound insights into how these elements affect the financial rationality of LLMs, indicating that targeted training and structured input methods could improve model performance. This work enriches our understanding of LLMs' strengths and weaknesses in financial applications, laying the groundwork for the development of more dependable and rational financial analysis tools.