Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning

Conference · Thu Aug 15 04:00:00 EDT 2024

DOI:https://doi.org/10.18653/v1/2024.sdp-1.8· OSTI ID:2484353

Munikoti, Sai ^[1]; Acharya, Anurag ^[1]; Wagle, Sridevi N. ^[1]; Horawalavithana, Yasanka S. ^[1]

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1. BATTELLE (PACIFIC NW LAB)

Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models helps to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2484353
• Report Number(s): PNNL-SA-189029
• Country of Publication: United States
• Language: English

Related Subjects

• Artificial intelligence (AI) / machine learning (ML)
Language Model
Reasoning