Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance. To tackle these issues, we introduce InvestorBench, the first benchmark specifically designed for evaluating LLM-based agents in diverse financial decision-making contexts. InvestorBench enhances the versatility of LLM-enabled agents by providing a comprehensive suite of tasks applicable to different financial products, including single equities like stocks and cryptocurrencies, and exchange-traded funds (ETFs). Additionally, we assess the reasoning and decision-making capabilities of our agent framework using thirteen different LLMs as backbone models, across various market environments and tasks. Furthermore, we have curated a diverse collection of open-source, datasets and developed a comprehensive suite of environments for financial decision-making. This establishes a highly accessible platform for evaluating financial agents’ performance across various scenarios.

Financial misinformation poses a significant threat to investment decisions and market stability. Recently, the application of Large Language Models (LLMs) for detecting financial misinformation has gained considerable attention within the natural language processing (NLP) community. The Financial Misinformation Detection (FMD) challenge @ Coling 2025 serves as a valuable platform for collaboration and innovation. This paper presents our solution to FMD challenge. Our approach involves using search engines to retrieve the summarized high-quality information as supporting evidence and designing a financial domain-specific chain-of-thought to enhance the reasoning capabilities of LLMs. We evaluated our method on both commercial closed-source LLMs (GPT-family) and open-source models (Llama-3.1-8B and QWen). The experimental results domonstrate that the proposed method improves veracity prediction performance. However, the quality of the generated explanations remains relatively poor. In the paper, we present the experimental findings and provides an in depth analysis of these results.

Large language models (LLMs) are increasingly widely used as critical components of knowledge retrieval systems and agentic systems. These systems can benefit from knowledge-seeking capabilities of LLMs, in other words, curiosity. However, this capability has not been evaluated quantitatively. Towards bridging this gap, we propose an evaluation framework, CDQG (Curiosity-Driven Question Generation). The CDQG task prompts LLMs to generate questions about a statement introducing scientific knowledge, simulating a curious person when facing the statement for the first time. The CDQG dataset contains 1,988 statements including physics, chemistry, and mathematics with distinct levels of difficulty, general knowledge statements, and intentionally erroneous statements. We score the qualities of the questions generated by LLMs along multiple dimensions. These scores are validated by rigorous controlled ablation studies and human evaluations. While large models like GPT-4 and Mistral 8x7b can generate highly coherent and relevant questions, the smaller Phi-2 model is equally or more effective. This indicates that size does not solely determine a model’s knowledge acquisition potential. CDQG quantifies a critical model capability, and opens up research opportunities for developing future knowledge retrieval systems driven by LLMs.

Large language models (LLMs) are increasingly being used for complex research tasks such as literature review, idea generation, and scientific paper analysis, yet their ability to truly understand and process the intricate relationships within complex research papers, such as the logical links between claims and supporting evidence remains largely unexplored. In this study, we present CLAIM-BENCH, a comprehensive benchmark for evaluating LLMs’ capabilities in scientific claim-evidence extraction and validation, a task that reflects deeper comprehension of scientific argumentation. We systematically compare three approaches which are inspired by divide and conquer approaches, across six diverse LLMs, highlighting model-specific strengths and weaknesses in scientific comprehension. Through evaluation involving over 300 claim-evidence pairs across multiple research domains, we reveal significant limitations in LLMs’ ability to process complex scientific content. Our results demonstrate that closed-source models like GPT-4 and Claude consistently outperform open-source counterparts in precision and recall across claim-evidence identification tasks. Furthermore, strategically designed three-pass and one-by-one prompting approaches significantly improve LLMs’ abilities to accurately link dispersed evidence with claims, although this comes at increased computational cost. CLAIM-BENCH sets a new standard for evaluating scientific comprehension in LLMs, offering both a diagnostic tool and a path forward for building systems capable of deeper, more reliable reasoning across full-length papers.