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Juanhui Li

7 accepted papers

2026

TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use

ICLR 2026poster

Large language model (LLM)-based agents increasingly rely on tool use to complete real-world tasks. While existing works evaluate the LLMs' tool use capability, they largely focus on the final answers yet overlook the detailed tool usage trajectory, i.e., whether tools are selected, parameterized, a…

Cited by 0SourcecodeScholar
2025

Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data

NAACL 2025findings

In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. However, the large size and high computation demands of LLMs limit their practicality in many applications, especially when further fine-tuning is required. To add…

2024

Mixture of Link Predictors on Graphs

NeurIPS 2024poster

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs)…

2024

Revisiting Link Prediction: a data perspective

ICLR 2024poster

Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. However, since datasets span a multitude of domains, they could have distinct underlying mechanisms of link formation. Evid…

2023

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?

ACL 2023long

Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completion (KGC) has become one of the most crucial tasks for KG research. Recently, considerable literature in this space has ce…

2023

Distance-Based Propagation for Efficient Knowledge Graph Reasoning

EMNLP 2023long main

Knowledge graph completion (KGC) aims to predict unseen edges in knowledge graphs (KGs), resulting in the discovery of new facts. A new class of methods have been proposed to tackle this problem by aggregating path information. These methods have shown tremendous ability in the task of KGC. However…

Cited by 0SourcecodeScholar
2023

Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking

NeurIPS 2023poster

Link prediction attempts to predict whether an unseen edge exists based on only a portion of the graph. A flurry of methods has been created in recent years that attempt to make use of graph neural networks (GNNs) for this task. Furthermore, new and diverse datasets have also been created to better…