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Found 22 papers, 19 papers with code
On the design space between molecular mechanics and machine learning force fields
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon.
ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
Only a few research explored the conflicts between the inherent knowledge of LLMs and the retrieved contextual knowledge.
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural language.
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
Motivated by this limit, we investigate building MoE models from existing dense large language models.
Timo: Towards Better Temporal Reasoning for Language Models
Therefore, we propose a crucial question: Can we build a universal framework to handle a variety of temporal reasoning tasks?
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
Mixture-of-Experts (MoE) models have shown remarkable capability in instruction tuning, especially when the number of tasks scales.
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world.
Probing Language Models for Pre-training Data Detection
Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase.
Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark
Therefore, Seal-Tools can serve as a new benchmark to evaluate the tool-calling ability of LLMs.
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets.
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation
To evaluate the efficacy of data augmentation methods for open-domain dialogue, we designed a clustering-based metric to characterize the semantic diversity of the augmented dialogue data.
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
An Investigation of Darwiche and Pearl's Postulates for Iterated Belief Update
Firstly, we present a modification of the original KM postulates based on belief states.
CED: Catalog Extraction from Documents
To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task.
Ranked #1 on
Catalog Extraction
on ChCatExt
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials
The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Neural Network Based in Silico Simulation of Combustion Reactions
Through further development, the algorithms in this study can be used to explore and discovery reaction mechanisms of many complex reaction systems, such as combustion, synthesis, and heterogeneous catalysis without any predefined reaction coordinates and elementary reaction steps.
ReacNetGenerator: an Automatic Reaction Network Generator for Reactive Molecular Dynamic Simulations
However, the analysis of the MD trajectories which contain thousands of species and reaction pathways has become a major obstacle to the application of reactive MD simulation in large-scale systems.
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
