2 citations · 2 across the 11 of their papers we have counts for
12 papers
SkillFM: Generating Skills for LLM Agents via Latent Flow Matching
Zuming Zhang, Jie He, Yizhe Zhang +1
Textual skills provide reusable guidance for large language model agents, but existing approaches often rely on manually curated skill banks or reinforcement learning with indirect…
CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
Jie He, Richard He Bai, Sinead Williamson +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimizati…
From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Jie He, Victor Gutiérrez-Basulto, Jeff Z. Pan
Reinforcement learning-based retrieval-augmented generation (RAG) methods enhance the reasoning abilities of large language models (LLMs). However, most rely only on final-answer r…
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning
Jie He, Simon Yu, Deyi Xiong +2
Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has be…
Similarity-Based Domain Adaptation with LLMs
Jie He, Wendi Zhou, Xiang Lorraine Li +1
Unsupervised domain adaptation leverages abundant labeled data from various source domains to generalize onto unlabeled target data. Prior research has primarily focused on learnin…
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation
Jie He, Jennifer Neville, Mengting Wan +6
Large Language Models (LLMs) can enhance their capabilities as AI assistants by integrating external tools, allowing them to access a wider range of information. While recent LLMs…
Evaluating and Improving Graph to Text Generation with Large Language Models
Jie He, Yijun Yang, Wanqiu Long +3
Large language models (LLMs) have demonstrated immense potential across various tasks. However, research for exploring and improving the capabilities of LLMs in interpreting graph…
MiCEval: Unveiling Multimodal Chain of Thought's Quality via Image Description and Reasoning Steps
Xiongtao Zhou, Jie He, Lanyu Chen +5
Multimodal Chain of Thought (MCoT) is a popular prompting strategy for improving the performance of multimodal large language models (MLLMs) across a range of complex reasoning tas…
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
Xiongtao Zhou, Jie He, Yuhua Ke +3
Multimodal large language models (MLLMs) fine-tuned with multimodal instruction datasets have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all par…
Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning
Simon Yu, Jie He, Pasquale Minervini +1
With the emergence of large language models, such as LLaMA and OpenAI GPT-3, In-Context Learning (ICL) gained significant attention due to its effectiveness and efficiency. However…
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing
Yijun Yang, Jie He, Pinzhen Chen +2
Several recent papers have investigated the potential of language models as knowledge bases as well as the existence of severe biases when extracting factual knowledge. In this wor…
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
Simon Yu, Jie He, Víctor Gutiérrez-Basulto +1
Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing a…