Exploring Scientific Hypothesis Generation with Mamba (2024)

Abstract: Generating scientifically grounded hypotheses is a challenging frontier task for generative AI models in science. The difficulty arises from the inherent subjectivity of the task and the extensive knowledge of prior work required to assess the validity of a generated hypothesis. Large Language Models (LLMs), trained on vast datasets from diverse sources, have shown a strong ability to utilize the knowledge embedded in their training data. Recent research has explored using transformer-based models for scientific hypothesis generation, leveraging their advanced capabilities. However, these models often require a significant number of parameters to manage Long sequences, which can be a limitation. State Space Models, such as Mamba, offer an alternative by effectively handling very Long sequences with fewer parameters than transformers. In this work, we investigate the use of Mamba for scientific hypothesis generation. Our preliminary findings indicate that Mamba achieves similar performance w.r.t. transformer-based models of similar sizes for a higher-order complex task like hypothesis generation. We have made our code available here: https://github.com/fglx-c/Exploring-Scientific-Hypothesis-Generation-with-Mamba

## introduction large language models (llms) have emerged as a cornerstone in artificial intelligence, particularly in scientific discovery. these models have been increasingly integrated into scientific hypothesis and idea generation, transforming traditional approaches to research. traditionally, the process of scientific hypothesis generation has involved a complex interplay of the scientific method and inductive reasoning, requiring meticulous observation, literature review, and identification of knowledge gaps. ## related work recent research has explored the potential of large language models (llms) in scientific hypothesis and idea generation, employing various approaches from direct prompting to more complex frameworks. @xcite and @xcite investigated the capabilities of gpt-3 and gpt-4 in generating hypotheses across diverse fields such as materials chemistry, physics, quantum information, and social psychology. while these models demonstrated broad knowledge and interdisciplinary insights, they often produced scientifically inaccurate outputs, highlighting the need for refined approaches. more sophisticated methods have emerged, integrating inter-domain translation, iterative processes, and adversarial techniques. the field-shift framework @xcite , for instance, utilized gpt-4 to translate concepts between neuroscience and developmental biology, successfully generating novel hypotheses and demonstrating potential for identifying symmetries across scientific domains. hypogenic @xcite employed a multi-armed bandit-inspired reward function to iteratively improve hypotheses, outperforming few-shot prompting across multiple tasks. in astronomy, @xcite applied adversarial prompting using multiple gpt-4 instances to generate, critique, and refine hypotheses, significantly improving their quality. further advancements in llm-based hypothesis generation have incorporated multi-agent approaches, causal graphs, knowledge graph-based retrieval augmentation, and novelty optimization. @xcite developed a collaborative framework where llm agents serve different roles (analyst, engineer, scientist, critic) in the hypothesis generation process. @xcite combined causal graphs extracted from psychology articles with llms to generate psychological hypotheses matching the novelty of human experts. the sci-mon framework @xcite generates novel research directions based on background contexts and a seed term used to constrain and guide the hypothesis space for the model. it employs an iterative novelty optimization workflow and various retrieval augmentations. gpt-4 produced the best results within this framework, although generated ideas still fell short of scientific literature in terms of depth. while previous work has primarily utilized transformer-based models, this study leverages mamba @xcite , a sequence modeling architecture based on selective state space models. mamba has demonstrated comparable or superior performance to transformer-based architectures, particularly with long sequences. by implementing our approach within the scimon framework, we aim to capitalize on mamba's strengths for improved hypothesis generation in scientific contexts, potentially addressing limitations observed in previous llm-based approaches. ## methodology as mentioned, our methodology is inspired by the scimon model. for our benchmarking study with mamba, we use the similar experimental framework as scimon. ## experiments & discussion we select t5 @xcite and gpt-4 as our baseline models to compare with mamba. we fine-tune various sizes of t5, mamba models and use a few short gpt-4 in parallel, with the finetuning process taking between 1 to 3 hours using eight h100 gpus. we present three evaluations: one using the automated metrics and the other with llm-as-judge (claude-3.5), following up with a long-text evaluation and finally an evaluation of generated output by a human. ## limitations and future work while this study provides valuable insights, it is important to acknowledge its limitations and potential areas for future research. the architecture of scimon introduces certain constraints that affect the scope and generalizability of our findings. one key limitation is the data scope, as scimon's dataset is exclusively composed of acl anthology papers from s2orc. this specialized focus may limit the applicability of our results to other scientific domains, particularly those that rely on multimodal data such as visual representations in biology or chemical structures in materials science. our comparative model analysis was restricted to an empirical comparison between mamba and transformer-based models under constrained parameter sizes. future work could benefit from more extensive comparisons involving larger parameter settings, which may reveal additional insights into the relative performance of these models in hypothesis generation tasks. furthermore, the rapid pace of development in state space models presents new opportunities for advancing hypothesis generation capabilities. recent innovations such as jamba @xcite , samba @xcite , and ttt @xcite were not included in our analysis but represent promising avenues for future research. investigating these emerging models could potentially uncover novel approaches to improve the efficiency and effectiveness of scientific hypothesis generation. ## memorization given that llms are trained on extensive datasets, including potentially the same sources used for evaluation, there is a risk that the models may reproduce memorized content rather than generating novel hypotheses. so, we conduct a memorization check to ensure the validation of our experiments. 1. @xcite shows that t5 is pretrained on c4 which was crawled from web prior to april 2019. given that the evaluation focuses on papers published in 2022, the chance that these papers are part of gpt-4's pretraining dataset is considerably low. furthermore, a manual review of gpt-4's outputs is conducted from scimon using a gold set composed of 2022 acl anthology papers. this review specifically looks for instances where gpt-4 might reproduce detailed information, such as method names, or generate text that closely mirrors the original papers. the findings show no significant evidence of memorization. ## conclusion our study provides insights into the application of language models, particularly the mamba architecture, for scientific hypothesis generation within the scimon model. comparative analysis reveals that mamba models perform comparably to t5 models of similar sizes, with mamba-790m achieving the highest scores in automatic evaluations. gpt-4, however, outperforms both in human and llmbased evaluations, demonstrating superior capability in generating relevant, novel, and scientifically valid hypotheses. mamba exhibits strength in processing longer input sequences, suggesting potential for complex scientific reasoning tasks. however, it shows instability with smaller inputs, indicating areas for improvement. these findings highlight the potential of state space models in advancing scientific hypothesis generation, despite limitations such as the use of only acl anthology papers and restricted parameter sizes in our analysis. future research should focus on expanding the dataset to diverse scientific domains, investigating larger parameter settings and emerging state space models, developing specialized benchmarks for longsequence processing, and addressing mamba's instability with smaller inputs. while mamba shows promise, particularly for long-context tasks, further research is needed to fully harness its potential and address limitations. as language models evolve, their integration into scientific workflows holds great promise for accelerating hypothesis generation and innovation across diverse fields. this research represents a significant step towards leveraging advanced language models to expand the frontiers of scientific inquiry and knowledge generation.