Papers by Hongyang Zhang
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees (2024.emnlp-main)
Copied to clipboard
| Challenge: | Modern Large Language Models (LLMs) are expensive and time-consuming. |
| Approach: | They propose a new technique of context-aware dynamic draft tree into drafting modeling. |
| Outcome: | The proposed method achieves speedup ratios of up to **5x**, which is 1.3x that of EAGLE. |
ParaSuite: Boosting LLM Reasoning via Paradox Resolution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for paradox research focus on checking basic logical consistency and not reflective reasoning. |
| Approach: | They propose a pipeline dedicated to paradox research that automates data synthesis, evaluation, and training. |
| Outcome: | The proposed pipeline improves paradoxical and general STEM reasoning. |
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a new algorithm to estimate fine-tuning performance for a target task is proposed . conventional subset selection methods require repeated training on subsets of auxiliary tasks . |
| Approach: | They propose an algorithm to fine-tune a language model for a target task by optimally using auxiliary tasks' information. |
| Outcome: | The proposed method can estimate fine-tuning performance on CPUs in seconds. |
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. |
| Approach: | They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models. |
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)
Copied to clipboard
Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation. |
| Approach: | They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance . |
| Outcome: | The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency. |
Long-form Hallucination Detection with Self-elicitation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection tend to decompose text into isolated statements, unable to understand contextual semantics. |
| Approach: | They propose a framework to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics. |
| Outcome: | The proposed framework enables self-elicitation to elicit expressions of knowledge and understand semantics. |
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking (2022.emnlp-main)
Copied to clipboard
| Challenge: | In information retrieval, candidate set pruning is used to speed up two-stage relevance ranking but lacks accurate error control and empirical guarantees. |
| Approach: | They propose a method that guarantees the test error after pruning is controlled under a user-specified threshold with high probability. |
| Outcome: | The proposed method reduces the average set size from 1000 to 27, increasing reranking speed by about 37 times while keeping MRR@10 greater than a pre-specified value of 0.38 with about 90% empirical coverage. |