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Found 7 papers, 6 papers with code
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.
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
In this survey, we comprehensively review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of MLLMs, introduce recent trends in applications of MLLMs on reasoning-intensive tasks, and finally discuss current practices and future directions.
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.
Are More Layers Beneficial to Graph Transformers?
Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow.
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.
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.
Domain Adaptation via Maximizing Surrogate Mutual Information
In the framework, a surrogate joint distribution models the underlying joint distribution of the unlabeled target domain.
