From Generalist to Specialist: A Survey of Large Language Models for Chemistry
- URL: http://arxiv.org/abs/2412.19994v1
- Date: Sat, 28 Dec 2024 03:40:25 GMT
- Title: From Generalist to Specialist: A Survey of Large Language Models for Chemistry
- Authors: Yang Han, Ziping Wan, Lu Chen, Kai Yu, Xin Chen,
- Abstract summary: Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP)
The predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry.
Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs.
- Score: 14.317448405387195
- License:
- Abstract: Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field.
Related papers
- ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models [62.37850540570268]
Existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals.
ChemEval identifies 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks.
Results show that while general LLMs excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge.
arXiv Detail & Related papers (2024-09-21T02:50:43Z) - ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area [50.15254966969718]
We introduce textbfChemVLM, an open-source chemical multimodal large language model for chemical applications.
ChemVLM is trained on a carefully curated bilingual dataset that enhances its ability to understand both textual and visual chemical information.
We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
arXiv Detail & Related papers (2024-08-14T01:16:40Z) - A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
Large language models (LLMs) have revolutionized the way text and other modalities of data are handled.
We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
arXiv Detail & Related papers (2024-06-16T08:03:24Z) - Are large language models superhuman chemists? [4.87961182129702]
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained.
Here, we introduce "ChemBench," an automated framework for evaluating the chemical knowledge and reasoning abilities of state-of-the-art LLMs.
We curated more than 2,700 question-answer pairs, evaluated leading open- and closed-source LLMs, and found that the best models outperformed the best human chemists.
arXiv Detail & Related papers (2024-04-01T20:56:25Z) - ChemLLM: A Chemical Large Language Model [49.308528569982805]
Large language models (LLMs) have made impressive progress in chemistry applications.
However, the community lacks an LLM specifically designed for chemistry.
Here, we introduce ChemLLM, a comprehensive framework that features the first LLM dedicated to chemistry.
arXiv Detail & Related papers (2024-02-10T01:11:59Z) - From Words to Molecules: A Survey of Large Language Models in Chemistry [8.129759559674968]
This paper explores the nuanced methodologies employed in integrating Large Language Models (LLMs) into the field of chemistry.
We categorize chemical LLMs into three distinct groups based on the domain and modality of their input data, and discuss approaches for integrating these inputs for LLMs.
Finally, we identify promising research directions, including further integration with chemical knowledge, advancements in continual learning, and improvements in model interpretability.
arXiv Detail & Related papers (2024-02-02T14:30:48Z) - Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension.
The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines.
As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
arXiv Detail & Related papers (2024-01-26T05:33:34Z) - Structured Chemistry Reasoning with Large Language Models [70.13959639460015]
Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in chemistry.
We introduce StructChem, a simple yet effective prompting strategy that offers the desired guidance and substantially boosts the LLMs' chemical reasoning capability.
Tests across four chemistry areas -- quantum chemistry, mechanics, physical chemistry, and kinetics -- StructChem substantially enhances GPT-4's performance, with up to 30% peak improvement.
arXiv Detail & Related papers (2023-11-16T08:20:36Z) - What can Large Language Models do in chemistry? A comprehensive
benchmark on eight tasks [41.9830989458936]
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged.
We aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.
arXiv Detail & Related papers (2023-05-27T14:17:33Z)
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.