ArxEval: Evaluating Retrieval and Generation in Language Models for Scientific Literature

Large Language Models (LLMs) have emerged as pivotal tools in information access and generation, particularly through their capabilities of producing factually accurate texts. As these models become increasingly integrated into various applications, ensuring the accuracy of their responses has become very important. The performance and reliability of LLMs in generating accurate information are significantly influenced by multiple factors, including training data quality, model architecture design, and post-training optimization processes [1], [2], [3]. However, a significant challenge in the deployment of LLMs lies in their propensity to generate nonfactual responses, a phenomenon commonly referred to as hallucination. These hallucinations fundamentally undermine the reliability and faithfulness of LLMs, presenting substantial obstacles to their widespread adoption across various domains [4], [5]. The mitigation of hallucinations has consequently emerged as a critical area of research within the field. While various strategies have been proposed and implemented to reduce hallucinations, showing promising improvements in the faithfulness of LLMs for general-purpose tasks, domain-specific applications remain particularly challenging [6], [7], [8]. In this paper, we present a comprehensive study evaluating the extent of hallucination in LLMs under domain-specific prompting, with a particular focus on scientific literature. We develop and implement a systematic evaluation pipeline to assess fifteen prominent open-source LLMs: Qwen 2.5 [9], Gemma 2 [10], Llama 3 [11], Phi 3 [12], Orca 2 [13], Mistral v-0.3 [14], Deepseek-llm [15], Olmo-2 [16], Mistral-Nemo [17], Eurus-2 [18], and Solar-Pro [19].