Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature

• URL: http://arxiv.org/abs/2304.05406v2
• Date: Tue, 12 Sep 2023 00:42:21 GMT
• Title: Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature
• Authors: Ioana Ciuc\u{a} and Yuan-Sen Ting
• Abstract summary: We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers. We employ a distillation technique that effectively reduces the size of the original input paper by 50%. We then explore the model's responses using a multi-document context.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers using in-context prompting. To optimize for efficiency, we employ a distillation technique that effectively reduces the size of the original input paper by 50\%, while maintaining the paragraph structure and overall semantic integrity. We then explore the model's responses using a multi-document context (ten distilled documents). Our findings indicate that GPT-4 excels in the multi-document domain, providing detailed answers contextualized within the framework of related research findings. Our results showcase the potential of large language models for the astronomical community, offering a promising avenue for further exploration, particularly the possibility of utilizing the models for hypothesis generation.

Related papers

• Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian [75.94354349994576]
  This paper explores the feasibility of employing smaller, domain-specific encoder LMs alongside prompting techniques to enhance performance in specialized contexts. Our study concentrates on the Italian bureaucratic and legal language, experimenting with both general-purpose and further pre-trained encoder-only models. The results indicate that while further pre-trained models may show diminished robustness in general knowledge, they exhibit superior adaptability for domain-specific tasks, even in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-07-30T08:50:16Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance. This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation. Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation [5.661396828160973]
  We conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples. We compare four large language models of varying sizes with different prompting techniques. Our findings show that the capabilities of large language models in triple verbalization can be significantly improved through few-shot prompting, post-processing, and efficient fine-tuning techniques.
  arXiv  Detail & Related papers  (2024-02-02T15:26:39Z)
• Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases [98.35348038111508]
  This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision) The core of our analysis delves into the distinct visual comprehension abilities of each model. Our findings illuminate the unique strengths and niches of both models.
  arXiv  Detail & Related papers  (2023-12-22T18:59:58Z)
• GPT Struct Me: Probing GPT Models on Narrative Entity Extraction [2.049592435988883]
  We evaluate the capabilities of two state-of-the-art language models -- GPT-3 and GPT-3.5 -- in the extraction of narrative entities. This study is conducted on the Text2Story Lusa dataset, a collection of 119 Portuguese news articles.
  arXiv  Detail & Related papers  (2023-11-24T16:19:04Z)
• Large Language Models for Propaganda Span Annotation [10.358271919023903]
  This study investigates whether Large Language Models, such as GPT-4, can effectively extract propagandistic spans. The experiments are performed over a large-scale in-house manually annotated dataset.
  arXiv  Detail & Related papers  (2023-11-16T11:37:54Z)
• OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement [11.602151258188862]
  We develop and evaluate multilingual scientific documents similarity measurement models in this work. We propose the first multilingual scientific documents dataset, Open-access Multilingual Scientific Documents (OpenMSD), which has 74M papers in 103 languages and 778 citation pairs.
  arXiv  Detail & Related papers  (2023-09-19T11:38:39Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy [0.0]
  We employ in-context prompting, supplying the model with up to 1000 papers from the NASA Astrophysics Data System. Our findings point towards a substantial boost in hypothesis generation when using in-context prompting. We illustrate how adversarial prompting empowers GPT-4 to extract essential details from a vast knowledge base to produce meaningful hypotheses.
  arXiv  Detail & Related papers  (2023-06-20T16:16:56Z)
• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities [92.79451009324268]
  We present CoAuthor, a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing. We demonstrate that CoAuthor can address questions about GPT-3's language, ideation, and collaboration capabilities. We discuss how this work may facilitate a more principled discussion around LMs' promises and pitfalls in relation to interaction design.
  arXiv  Detail & Related papers  (2022-01-18T07:51:57Z)
• Explaining Relationships Between Scientific Documents [55.23390424044378]
  We address the task of explaining relationships between two scientific documents using natural language text. In this paper we establish a dataset of 622K examples from 154K documents.
  arXiv  Detail & Related papers  (2020-02-02T03:54:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.