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Found 6 papers, 5 papers with code
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions.
DevEval: Evaluating Code Generation in Practical Software Projects
Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.
Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models
With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred.
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference.
How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models.
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently.
