Search Results for author:
Found 15 papers, 13 papers with code
BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning
The applications of large language models (LLMs) in various biological domains have been explored recently, but their reasoning ability in complex biological systems, such as pathways, remains underexplored, which is crucial for predicting biological phenomena, formulating hypotheses, and designing experiments.
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
Our survey organizes the findings into three core components: Game Framework, Social Agent, and Evaluation Protocol.
Non-myopic Generation of Language Models for Reasoning and Planning
Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps.
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
Recent advancements in computational chemistry have increasingly focused on synthesizing molecules based on textual instructions.
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
Empowering Large Language Model Agents through Action Learning
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior.
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models
We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning.
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
Evaluating Large Language Models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications.
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks.
A Challenging Benchmark for Low-Resource Learning
Experiments on a wide range of models show that neural networks, even pre-trained language models, have sharp performance drops on our benchmark, demonstrating the effectiveness on evaluating the weaknesses of neural networks.
Retrieved Sequence Augmentation for Protein Representation Learning
RSA links query protein sequences to a set of sequences with similar structures or properties in the database and combines these sequences for downstream prediction.
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
Certified Robustness Against Natural Language Attacks by Causal Intervention
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
Domain Adaptation via Maximizing Surrogate Mutual Information
In the framework, a surrogate joint distribution models the underlying joint distribution of the unlabeled target domain.
