Fetching the paper…
Reading the bibliography…
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.
“Taxonomy of Educational Objectives: The Classification of Educational Goals”, Taxonomy of Educational Objectives: The Classification of Educational Goals v. 1
B.S. Bloom · 1956
Earlier work this paper cites.
“ZINC: A Free Tool to Discover Chemistry for Biology”
J.. Irwin et al · 2012
Earlier work this paper cites.
“An Additive Definition of Molecular Complexity”
T. Böttcher · 2016
Earlier work this paper cites.
“MoleculeNet: a benchmark for molecular machine learning”
Z. Wu et al · 2018
Earlier work this paper cites.
A. Togo, K. Shinohara and I. Tanaka · 2018
Earlier work this paper cites.
“The Matter Simulation (R)evolution”
A. Aspuru-Guzik, R. Lindh and M. Reiher · 2018
Earlier work this paper cites.
“Language Models are Few-Shot Learners”
T.. Brown et al · 2020
Earlier work this paper cites.
“Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm”
A. Dunn et al · 2020
Earlier work this paper cites.
“Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm”
A. Dunn et al · 2020
Earlier work this paper cites.
“On the dangers of stochastic parrots: Can language models be too big?”
E.. Bender, T. Gebru, A. McMillan-Major and S. Shmitchell · 2021
Earlier work this paper cites.
“On the Opportunities and Risks of Foundation Models”
R. Bommasani et al · 2021
Earlier work this paper cites.
“Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development”
K. Huang et al · 2021
Earlier work this paper cites.
“ChemistryQA: A Complex Question Answering Dataset from Chemistry”, 2021
Z. Wei et al · 2021
Earlier work this paper cites.
“MAYGEN: an open-source chemical structure generator for constitutional isomers based on the orderly generation principle”
M.. Yirik, M. Sorokina and C. Steinbeck · 2021
Earlier work this paper cites.
“Mapping stellar surfaces III: An Efficient, Scalable, and Open-Source Doppler Imaging Model”
R. Luger et al · 2021
Earlier work this paper cites.
“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”
C.. Northcutt, A. Athalye and J. Mueller · 2021
Earlier work this paper cites.
“DiSCoMaT: distantly supervised composition extraction from tables in materials science articles”
T. Gupta, M. Zaki and N. Krishnan · 2022
Earlier work this paper cites.
“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”
D. Ganguli et al · 2022
Earlier work this paper cites.
“Dual use of artificial-intelligence-powered drug discovery”
F. Urbina, F. Lentzos, C. Invernizzi and S. Ekins · 2022
Earlier work this paper cites.
“A teachable moment for dual-use”
F. Urbina, F. Lentzos, C. Invernizzi and S. Ekins · 2022
Earlier work this paper cites.
“Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models”
Aarohi Srivastava et al · 2022
Earlier work this paper cites.
“ChemBERTa-2: Towards Chemical Foundation Models”
W. Ahmad et al · 2022
Earlier work this paper cites.
“Lift: Language-interfaced fine-tuning for non-language machine learning tasks”
T. Dinh et al · 2022
Earlier work this paper cites.
“Holistic Evaluation of Language Models”
P. Liang et al · 2022
Earlier work this paper cites.
“Galactica: A Large Language Model for Science”
R. Taylor et al · 2022
Earlier work this paper cites.
E. Karpas et al · 2022
Earlier work this paper cites.
“React: Synergizing reasoning and acting in language models”
S. Yao et al · 2022
Earlier work this paper cites.
“PubChem 2023 update”
S. Kim et al · 2022
Earlier work this paper cites.
“Galactica: A Large Language Model for Science”
R. Taylor et al · 2022
Earlier work this paper cites.
E. Karpas et al · 2022
Earlier work this paper cites.
“React: Synergizing reasoning and acting in language models”
S. Yao et al · 2022
Earlier work this paper cites.
“Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models”
Aarohi Srivastava et al · 2022
Earlier work this paper cites.
“LangChain”, 2022
Harrison Chase · 2022
Earlier work this paper cites.
“Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models”
T.. Kung et al · 2023
Cited alongside, same era.
“Autonomous chemical research with large language models”
D.. Boiko, R. MacKnight, B. Kline and G. Gomes · 2023
Cited alongside, same era.
“Sparks of Artificial General Intelligence: Early experiments with GPT-4”
S. Bubeck et al · 2023
Cited alongside, same era.
R. McCoy et al · 2023
Cited alongside, same era.
“Frontier AI regulation: Managing emerging risks to public safety”
M. Anderljung et al · 2023
Cited alongside, same era.
“What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks”
T. Guo et al · 2023
Later among the works it cites.
“SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research”
L. Sun et al · 2023
Later among the works it cites.
“GPQA: A Graduate-Level Google-Proof Q&A Benchmark”
David Rein et al · 2023
Later among the works it cites.
“Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”
Jason Wei et al · 2023
Later among the works it cites.
“Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4”
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
Cited alongside, same era.
“The future of chemistry is language”
A.. White · 2023
Cited alongside, same era.
“14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon”
K.. Jablonka et al · 2023
Cited alongside, same era.
“Bayesian Optimization of Catalysts With In-context Learning”
M.. Ramos, S.. Michtavy, M.. Porosoff and A.. White · 2023
Cited alongside, same era.
A.. Rubungo, C. Arnold, B.. Rand and A.. Dieng · 2023
Cited alongside, same era.
D. Flam-Shepherd and A. Aspuru-Guzik · 2023
Cited alongside, same era.
“Automatic extraction of FAIR data from publications using LLM”
L. Patiny and G. Godin · 2023
Cited alongside, same era.
M. Xiong et al · 2023
Later among the works it cites.
“Benchmarking Large Language Models for Molecule Prediction Tasks”
Z. Zhong, K. Zhou and D. Mottin · 2024
Closest in time.
OpenAI et al · 2024
Closest in time.
“Augmenting large language models with chemistry tools”
Andres M. et al · 2024
Closest in time.
“ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization”
K. Darvish et al · 2024
Closest in time.
“Leveraging large language models for predictive chemistry”
Kevin Jablonka, Philippe Schwaller, Andres Ortega-Guerrero and Berend Smit · 2024
Closest in time.
“Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules”
Z. Xie et al · 2024
Closest in time.
“From Words to Molecules: A Survey of Large Language Models in Chemistry”
C. Liao, Y. Yu, Y. Mei and Y. Wei · 2024
Closest in time.
“ChemLLM: A Chemical Large Language Model”
D. Zhang et al · 2024
Closest in time.
A. Kristiadi et al · 2024
Closest in time.
“Fine-Tuned Language Models Generate Stable Inorganic Materials as Text”
N. Gruver et al · 2024
Closest in time.
“MatText: Do Language Models Need More than Text & Scale for Materials Modeling?”
Nawaf Alampara, Santiago Miret and Kevin Jablonka · 2024
Closest in time.
“Structured information extraction from scientific text with large language models”
John Dagdelen et al · 2024
Closest in time.
“Image and data mining in reticular chemistry powered by GPT-4V”
Zhiling Zheng et al · 2024
Closest in time.
“Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning”
J Caufield et al · 2024
Closest in time.
“From Text to Insight: Large Language Models for Materials Science Data Extraction”
Mara Schilling-Wilhelmi et al · 2024
Closest in time.
“Language agents achieve superhuman synthesis of scientific knowledge”
Michael Skarlinski et al · 2024
Closest in time.
“Are LLMs Ready for Real-World Materials Discovery?”
S. Miret and N. Krishnan · 2024
Closest in time.
“Comprehensive evaluation of molecule property prediction with ChatGPT”
X. Cai et al · 2024
Closest in time.
“MaScQA: investigating materials science knowledge of large language models”
M. Zaki, Jayadeva, Mausam and N… Krishnan · 2024
Closest in time.
“Towards Understanding Factual Knowledge of Large Language Models”
Xuming Hu et al · 2024
Closest in time.
“OMNI: Open-endedness via Models of human Notions of Interestingness”
Jenny Zhang, Joel Lehman, Kenneth Stanley and Jeff Clune · 2024
Closest in time.
“tinyBenchmarks: evaluating LLMs with fewer examples”
F.. Polo et al · 2024
Closest in time.
“Toolformer: Language models can teach themselves to use tools”
T. Schick et al · 2024
Closest in time.
“Open-Endedness is Essential for Artificial Superhuman Intelligence”
Edward Hughes et al · 2024
Closest in time.
“Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering”
L. Schulze et al · 2024
Closest in time.
“Toolformer: Language models can teach themselves to use tools”
T. Schick et al · 2024
Closest in time.
“Comprehensive evaluation of molecule property prediction with ChatGPT”
X. Cai et al · 2024
Closest in time.