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Deriq-Qian-Dong authored Nov 19, 2024
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# DO NOT EDIT, GENERATED AUTOMATICALLY FROM SOURCES.YAML (AND ELSEWHERE)
# See https://github.com/greenelab/lab-website-template/wiki/Citations

- id: interpret the internal states of recommendation model with sparse autoencoder
title: Interpret the Internal States of Recommendation Model with Sparse Autoencoder
authors:
- Jiayin Wang
- Xiaoyu Zhang
- Weizhi Ma
- Min Zhang
publisher: arXiv preprint arXiv:2411.06112
date: '2024-11-09'
link: https://scholar.google.com/scholar?oi=bibs&cluster=18077587377125200085&btnI=1&hl=en
- id: a user-centric multi-intent benchmark for evaluating large language models
title: A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
authors:
- Jiayin Wang
- Fengran Mo
- Weizhi Ma
- Peijie Sun
- Min Zhang
- Jian-Yun Nie
publisher: Proceedings of the 2024 Conference on Empirical Methods in Natural Language
Processing
date: '2024-11-01'
link: https://scholar.google.com/scholar?oi=bibs&cluster=618902460846252148&btnI=1&hl=en
- id: 'beyond utility: evaluating llm as recommender'
title: 'Beyond Utility: Evaluating LLM as Recommender'
authors:
- Chumeng Jiang
- Jiayin Wang
- Weizhi Ma
- Charles LA Clarke
- Shuai Wang
- Chuhan Wu
- Min Zhang
publisher: arXiv preprint arXiv:2411.00331
date: '2024-11-01'
link: https://scholar.google.com/scholar?oi=bibs&cluster=13691887376177201280&btnI=1&hl=en
- id: 'stard: a chinese statute retrieval dataset derived from real-life queries by
non-professionals'
title: 'STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries
by Non-professionals'
authors:
- Weihang Su
- Yiran Hu
- Anzhe Xie
- Qingyao Ai
- Quezi Bing
- Ning Zheng
- Yun Liu
- Weixing Shen
- Yiqun Liu
publisher: 'Findings of the Association for Computational Linguistics: EMNLP 2024'
date: '2024-11-01'
link: https://scholar.google.com/scholar?oi=bibs&cluster=17170071477068419021&btnI=1&hl=en
- id: 'persrv: personalized sticker retrieval with vision-language model'
title: 'PerSRV: Personalized Sticker Retrieval with Vision-Language Model'
authors:
- Heng Er Metilda Chee
- Jiayin Wang
- Zhiqiang Guo
- Weizhi Ma
- Min Zhang
publisher: arXiv preprint arXiv:2410.21801
date: '2024-10-29'
link: https://scholar.google.com/scholar?oi=bibs&cluster=13051186181124067386&btnI=1&hl=en
- id: 'brain-computer interface meets information retrieval: perspective on next-generation
information system'
title: 'Brain-Computer Interface Meets Information Retrieval: Perspective on Next-generation
Information System'
authors:
- Ziyi Ye
- Qingyao Ai
- Yiqun Liu
publisher: Proceedings of the 1st International Workshop on Brain-Computer Interfaces
(BCI) for Multimedia Understanding
date: '2024-10-28'
link: https://scholar.google.com/scholar?oi=bibs&cluster=2729248998115225523&btnI=1&hl=en
- id: query augmentation with brain signals
title: Query Augmentation with Brain Signals
authors:
- Ziyi Ye
- Jingtao Zhan
- Qingyao Ai
- Yiqun Liu
- Maarten de Rijke
- Christina Lioma
- Tuukka Ruotsalo
publisher: Proceedings of the 32nd ACM International Conference on Multimedia
date: '2024-10-28'
link: https://scholar.google.com/scholar?oi=bibs&cluster=18138185575064609128&btnI=1&hl=en
- id: automatic large language model evaluation via peer review
title: Automatic Large Language Model Evaluation via Peer Review
authors:
- Zhumin Chu
- Qingyao Ai
- Yiteng Tu
- Haitao Li
- Yiqun Liu
publisher: Proceedings of the 33rd ACM International Conference on Information and
Knowledge Management
date: '2024-10-21'
link: https://scholar.google.com/scholar?oi=bibs&cluster=3715084288291653195&btnI=1&hl=en
- id: 'calibraeval: calibrating prediction distribution to mitigate selection bias
in llms-as-judges'
title: 'CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias
in LLMs-as-Judges'
authors:
- Haitao Li
- Junjie Chen
- Qingyao Ai
- Zhumin Chu
- Yujia Zhou
- Qian Dong
- Yiqun Liu
publisher: arXiv preprint arXiv:2410.15393
date: '2024-10-20'
link: https://scholar.google.com/scholar?oi=bibs&cluster=4984174168343474622&btnI=1&hl=en
- id: predicting gate operation in open canal control with multitask sequential model
title: Predicting Gate Operation in Open Canal Control With Multitask Sequential
Model
authors:
- Shiyuan Guo
- Weizhi Ma
- Zhigang Yang
- Jinlong Liu
- Zhongjing Wang
- Min Zhang
publisher: IEEE Transactions on Industrial Informatics
date: '2024-10-17'
link: https://scholar.google.com/scholar?oi=bibs&cluster=16998002757204237141&btnI=1&hl=en
- id: an automatic and cost-efficient peer-review framework for language generation
evaluation
title: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation
Evaluation
authors:
- Junjie Chen
- Weihang Su
- Zhumin Chu
- Haitao Li
- Qinyao Ai
- Yiqun Liu
- Min Zhang
- Shaoping Ma
publisher: arXiv preprint arXiv:2410.12265
date: '2024-10-16'
link: https://scholar.google.com/scholar?oi=bibs&cluster=9153674731009643979&btnI=1&hl=en
- id: 'steptool: a step-grained reinforcement learning framework for tool learning
in llms'
title: 'StepTool: A Step-grained Reinforcement Learning Framework for Tool Learning
in LLMs'
authors:
- Yuanqing Yu
- Zhefan Wang
- Weizhi Ma
- Zhicheng Guo
- Jingtao Zhan
- Shuai Wang
- Chuhan Wu
- Zhiqiang Guo
- Min Zhang
publisher: arXiv preprint arXiv:2410.07745
date: '2024-10-10'
link: https://scholar.google.com/scholar?oi=bibs&cluster=1516482186641552345&btnI=1&hl=en
- id: large language models as evaluators for recommendation explanations
title: Large Language Models as Evaluators for Recommendation Explanations
authors:
- Xiaoyu Zhang
- Yishan Li
- Jiayin Wang
- Bowen Sun
- Weizhi Ma
- Peijie Sun
- Min Zhang
publisher: Proceedings of the 18th ACM Conference on Recommender Systems
date: '2024-10-08'
link: https://scholar.google.com/scholar?oi=bibs&cluster=18372768278974710976&btnI=1&hl=en
- id: 'right tool, right job: recommendation for repeat and exploration consumption
in food delivery'
title: 'Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption
in Food Delivery'
authors:
- Jiayu Li
- Aixin Sun
- Weizhi Ma
- Peijie Sun
- Min Zhang
publisher: Proceedings of the 18th ACM Conference on Recommender Systems
date: '2024-10-08'
link: https://scholar.google.com/scholar?oi=bibs&cluster=14287885740564190611&btnI=1&hl=en
- id: 'beyond scalar reward model: learning generative judge from preference data'
title: 'Beyond Scalar Reward Model: Learning Generative Judge from Preference Data'
authors:
- Ziyi Ye
- Xiangsheng Li
- Qiuchi Li
- Qingyao Ai
- Yujia Zhou
- Wei Shen
- Dong Yan
- Yiqun Liu
publisher: arXiv preprint arXiv:2410.03742
date: '2024-10-01'
link: https://scholar.google.com/scholar?oi=bibs&cluster=16395162004016382741&btnI=1&hl=en
- id: 'lexeval: a comprehensive chinese legal benchmark for evaluating large language
models'
title: 'LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language
Models'
authors:
- Haitao Li
- You Chen
- Qingyao Ai
- Yueyue Wu
- Ruizhe Zhang
- Yiqun Liu
publisher: arXiv preprint arXiv:2409.20288
date: '2024-09-30'
link: https://scholar.google.com/scholar?oi=bibs&cluster=7968372114126733363&btnI=1&hl=en
- id: "comparing point\u2010wise and pair\u2010wise relevance judgment with brain\
\ signals"
title: "Comparing point\u2010wise and pair\u2010wise relevance judgment with brain\
Expand Down Expand Up @@ -116,18 +332,6 @@
and Development in Information Retrieval
date: '2024-07-10'
link: https://scholar.google.com/scholar?oi=bibs&cluster=11194931778520386668&btnI=1&hl=en
- id: 'macrec: a multi-agent collaboration framework for recommendation'
title: 'MACRec: A Multi-Agent Collaboration Framework for Recommendation'
authors:
- Zhefan Wang
- Yuanqing Yu
- Wendi Zheng
- Weizhi Ma
- Min Zhang
publisher: Proceedings of the 47th International ACM SIGIR Conference on Research
and Development in Information Retrieval
date: '2024-07-10'
link: https://scholar.google.com/scholar?oi=bibs&cluster=2055601099317038213&btnI=1&hl=en
- id: unbiased learning-to-rank needs unconfounded propensity estimation
title: Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation
authors:
Expand All @@ -142,6 +346,18 @@
and Development in Information Retrieval
date: '2024-07-10'
link: https://scholar.google.com/scholar?oi=bibs&cluster=434985736173913319&btnI=1&hl=en
- id: 'macrec: a multi-agent collaboration framework for recommendation'
title: 'MACRec: A Multi-Agent Collaboration Framework for Recommendation'
authors:
- Zhefan Wang
- Yuanqing Yu
- Wendi Zheng
- Weizhi Ma
- Min Zhang
publisher: Proceedings of the 47th International ACM SIGIR Conference on Research
and Development in Information Retrieval
date: '2024-07-10'
link: https://scholar.google.com/scholar?oi=bibs&cluster=2055601099317038213&btnI=1&hl=en
- id: prompt refinement with image pivot for text-to-image generation
title: Prompt Refinement with Image Pivot for Text-to-Image Generation
authors:
Expand Down Expand Up @@ -618,6 +834,36 @@
publisher: arXiv preprint arXiv:2401.15641
date: '2024-01-28'
link: https://scholar.google.com/scholar?oi=bibs&cluster=603756173351366052&btnI=1&hl=en
- id: 'ledqa: a chinese legal case document-based question answering dataset'
title: 'LeDQA: A Chinese Legal Case Document-based Question Answering Dataset'
authors:
- Bulou Liu
- Zhenhao Zhu
- Qingyao Ai
- Yiqun Liu
- Yueyue Wu
publisher: Legal question answering based on case documents is a pivotal legal AI
application and helps extract key elements from the legal case documents to promote
downstream tasks. Intuitively, the form of this task is similar to legal machine
reading comprehension. However, in existing legal machine reading comprehension
datasets, the background information is much shorter than the legal case documents,
and the questions are not designed from the perspective of legal knowledge. In
this paper, we present LeDQA 1, the first Chinese legal case document-based question
answering dataset to our best knowledge. Specifically, we build a comprehensive
question schema (including 48 element-based questions) for the Chinese civil law
by legal professionals. And considering the cost of human annotations are too
expensive, we use one of the SOTA LLMs (ie, GPT-4) to annotate the relevant sentences
to these questions in each case document. The constructed dataset originates from
Chinese civil cases and contains 100 case documents, 4,800 casequestion pairs
and 132,048 sentence-level relevance annotations. We implement several text matching
algorithms for relevant sentence selection and various Large Language Models (LLMs)
for legal question answering on LeDQA. The experimental results indicate that
incorporating relevant sentences can benefit the performance of question answering
models, but further efforts are still required to address the remaining challenges
such as retrieving irrelevant sentences and incorrect reasoning between retrieved
sentences.
date: '2024-01-01'
link: https://scholar.google.com/scholar?oi=bibs&cluster=12507360554455884684&btnI=1&hl=en
- id: combining multiple supervision for robust zero-shot dense retrieval
title: Combining Multiple Supervision for Robust Zero-shot Dense Retrieval
authors:
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