LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation

• URL: http://arxiv.org/abs/2401.17244v3
• Date: Wed, 09 Oct 2024 20:13:51 GMT
• Title: LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
• Authors: Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, Janosh Riebesell,
• Abstract summary: Large Language Models (LLMs) inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. Here we introduce LLaMP, a framework of hierarchical reasoning-and-acting (RAG) agents that can interact with computational and experimental data. Without fine-tuning, LLaMP demonstrates strong tool usage ability to comprehend and integrate various modalities of materials science concepts.
• Score: 0.0
• License:
• Abstract: Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. Here we introduce LLaMP, a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can dynamically and recursively interact with computational and experimental data on Materials Project (MP) and run atomistic simulations via high-throughput workflow interface. Without fine-tuning, LLaMP demonstrates strong tool usage ability to comprehend and integrate various modalities of materials science concepts, fetch relevant data stores on the fly, process higher-order data (such as crystal structure and elastic tensor), and streamline complex tasks in computational materials and chemistry. We propose a simple metric combining uncertainty and confidence estimates to evaluate the self-consistency of responses by LLaMP and vanilla LLMs. Our benchmark shows that LLaMP effectively mitigates the intrinsic bias in LLMs, counteracting the errors on bulk moduli, electronic bandgaps, and formation energies that seem to derive from mixed data sources. We also demonstrate LLaMP's capability to edit crystal structures and run annealing molecular dynamics simulations using pre-trained machine-learning force fields. The framework offers an intuitive and nearly hallucination-free approach to exploring and scaling materials informatics, and establishes a pathway for knowledge distillation and fine-tuning other language models. Code and live demo are available at https://github.com/chiang-yuan/llamp

Related papers

• Can a Large Language Model Learn Matrix Functions In Context? [3.7478782183628634]
  Large Language Models (LLMs) have demonstrated the ability to solve complex tasks through In-Context Learning (ICL) This paper explores the capacity of LLMs to solve non-linear numerical computations, with specific emphasis on functions of the Singular Value Decomposition.
  arXiv  Detail & Related papers  (2024-11-24T00:33:43Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training [9.023648972811458]
  RagVL is a novel framework with knowledge-enhanced reranking and noise-injected training. We instruction-tune the MLLM with a simple yet effective instruction template to induce its ranking ability. For generation, we inject visual noise during training at the data and token levels to enhance the generator's robustness.
  arXiv  Detail & Related papers  (2024-07-31T08:43:17Z)
• Benchmarking Large Language Models for Molecule Prediction Tasks [7.067145619709089]
  Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. This paper explores a fundamental question: Can LLMs effectively handle molecule prediction tasks? We identify several classification and regression prediction tasks across six standard molecule datasets. We compare their performance with existing Machine Learning (ML) models, which include text-based models and those specifically designed for analysing the geometric structure of molecules.
  arXiv  Detail & Related papers  (2024-03-08T05:59:56Z)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• Generative Multimodal Entity Linking [24.322540112710918]
  Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. We propose GEMEL, a Generative Multimodal Entity Linking framework based on Large Language Models (LLMs) Our framework is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution.
  arXiv  Detail & Related papers  (2023-06-22T07:57:19Z)
• Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models [75.75038268227554]
  Self-Checker is a framework comprising a set of plug-and-play modules that facilitate fact-checking. This framework provides a fast and efficient way to construct fact-checking systems in low-resource environments.
  arXiv  Detail & Related papers  (2023-05-24T01:46:07Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

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.