[Paper Review] SciMON: Scientific Inspiration Machines Optimized for Novelty

Qingyun Wang, Doug Downey+2 more|arXiv (Cornell University)|May 23, 2023

Advanced Text Analysis Techniques48 references11 citations

TL;DR

SciMON proposes a framework that retrieves literature-based inspirations and uses iterative novelty boosting to generate novel, literature-grounded scientific ideas from problem contexts, improving over standard LLM outputs but noting remaining gaps in depth and utility.

ABSTRACT

We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction--severely limiting the expressivity of hypotheses. This line of work also does not focus on optimizing novelty. We take a dramatic departure with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natural language ideas grounded in literature. We present SciMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and updating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature

Motivation & Objective

• Motivate (and formalize) a setting where AI generates novel scientific directions grounded in literature rather than simple binary links.
• Create a data-driven pipeline to train and evaluate models on generating ideas from problem contexts.
• Develop an iterative novelty optimization mechanism to push generated ideas away from existing literature while staying relevant.

Proposed method

• Collect and preprocess a large corpus of papers to extract background/problem sentences and corresponding ideas using scientific information extraction (IE).
• Construct background contexts and seed terms, and retrieve inspirations from semantic-neighbor, knowledge-graph, and citation-based sources.
• Generate ideas with LLMs (GPT-3.5/4, T5) using in-context learning and optional fine-tuning, enhanced with an in-context contrastive objective to reduce copying from the background.
• Implement an iterative novelty boosting loop that retrieves similar ideas, scores novelty against a reference corpus, and updates ideas to improve novelty until a threshold is met.
• Introduce a novelty-penalty mechanism and use retrieved related work as negative prompts to encourage more distinct ideas.
• Evaluate using human studies across NLP and biomedical domains to assess relevance, novelty, and technical depth.

Experimental results

Research questions

• RQ1 How can problem-context input be transformed into novel ideas grounded in literature?
• RQ2 Can retrieval of inspirations from literature and iterative novelty boosting improve the novelty and technical depth of generated ideas compared to baseline LLMs?
• RQ3 What are the limitations of current LLMs in generating scientific ideas, and how can retrieval-augmented methods mitigate them?
• RQ4 How transferable is SciMON across domains (e.g., NLP/AI and biomedical)?

Key findings

• GPT-4-based outputs can be more verbose and sometimes more helpful, but overall show limited novelty and technical depth without augmentation.
• Retrieval-augmented generation with semantic neighbors, knowledge graphs, and citation-based inspirations improves novelty and depth compared with baselines.
• Iterative novelty boosting (retrieve-compare-update) increases novelty in a significant fraction of cases (e.g., first iteration yielding substantial novelty in a majority of updates).
• In-domain and cross-domain (NLP and biomedical) experiments indicate improved idea quality, but ground-truth ideas remain markedly more novel and detailed than generated ones.
• Human evaluations show that GPT-4 with KG and SN augmentations outperforms other baselines, though ideas still trail ground-truth papers in novelty and technical depth.