Papers by Ru Peng
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
DORY: Deliberative Prompt Recovery for LLM (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are used for their groundbreaking performance across various tasks. |
| Approach: | They propose a method that leverages uncertainty to recover prompts accurately using a single LLM without external resources or models. |
| Outcome: | The proposed approach outperforms baselines across diverse LLMs and prompt benchmarks and establishes a new state-of-the-art record in prompt recovery tasks. |
LLM-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge retrieval methods for task-oriented dialogues are limited by data scarcity and lack of data to annotate. |
| Approach: | They propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue . they propose to select the most relevant knowledge from retrieved top-K records and incorporate them as prompts to guide a generator in response generation. |
| Outcome: | The proposed model outperforms state-of-the-art in three benchmarks on three standard benchmarks. |
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Distill The Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multimodal machine translation (MMT) have focused on the fusion and alignment of images and texts to improve MMT. |
| Approach: | They propose an image-free inference framework that supports image-based inference via an inversion knowledge distillation scheme. |
| Outcome: | The proposed framework is the first to rival or surpass image-must frameworks on the multimodal translation benchmark. |
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required. |
| Approach: | They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output. |
| Outcome: | The proposed model reduces time and space costs while enabling seamless online deployment. |
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)
Copied to clipboard
Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao
| Challenge: | High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. |
| Approach: | They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth. |
| Outcome: | the proposed pipeline outperforms 14 leading baselines on 16 benchmarks. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |