3 code implementations • 15 Nov 2023 • Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.
1 code implementation • 25 Jan 2024 • Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, Dong Yu
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents.
1 code implementation • 9 Nov 2023 • Shuyi Xie, Wenlin Yao, Yong Dai, Shaobo Wang, Donlin Zhou, Lifeng Jin, Xinhua Feng, Pengzhi Wei, Yujie Lin, Zhichao Hu, Dong Yu, Zhengyou Zhang, Jing Nie, Yuhong Liu
We construct a hierarchical task tree encompassing 7 major areas covering over 200 categories and over 800 tasks, which covers diverse capabilities such as question answering, reasoning, multiturn dialogue, and text generation, to evaluate LLMs in a comprehensive and in-depth manner.
1 code implementation • 7 Jan 2024 • Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, PengFei Liu, Dong Yu
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
1 code implementation • 22 Oct 2022 • Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #7 on Abstractive Text Summarization on CNN / Daily Mail
1 code implementation • 13 Mar 2024 • Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
2 code implementations • EMNLP 2021 • Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu
We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.
1 code implementation • 29 Jun 2023 • Xuansheng Wu, Huachi Zhou, Yucheng Shi, Wenlin Yao, Xiao Huang, Ninghao Liu
To evaluate our approach, we introduce a cold-start recommendation benchmark, and the results demonstrate that the enhanced small language models can achieve comparable cold-start recommendation performance to that of large models with only $17\%$ of the inference time.
1 code implementation • 9 Nov 2022 • Hongming Zhang, Wenlin Yao, Dong Yu
We argue that using the static embedding of the event type name might not be enough because a single word could be ambiguous, and we need a sentence to define the type semantics accurately.
1 code implementation • ACL 2022 • Xiang Yue, Xiaoman Pan, Wenlin Yao, Dian Yu, Dong Yu, Jianshu Chen
And with our pretrained reader, the entire system improves by up to 4% in exact match.
1 code implementation • ACL 2022 • Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen
Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.
1 code implementation • 24 May 2023 • James Y. Huang, Wenlin Yao, Kaiqiang Song, Hongming Zhang, Muhao Chen, Dong Yu
It is unclear whether the compositional semantics of sentences can be directly reflected as compositional operations in the embedding space.
1 code implementation • 21 Oct 2022 • Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.
Ranked #2 on Visual Commonsense Tests on ViComTe-color
1 code implementation • 4 Oct 2020 • Wenlin Yao, Cheng Zhang, Shiva Saravanan, Ruihong Huang, Ali Mostafavi
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.
1 code implementation • 2 Dec 2022 • Chao Zhao, Faeze Brahman, Kaiqiang Song, Wenlin Yao, Dian Yu, Snigdha Chaturvedi
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
1 code implementation • 6 Dec 2022 • Pei Chen, Wenlin Yao, Hongming Zhang, Xiaoman Pan, Dian Yu, Dong Yu, Jianshu Chen
However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base.
1 code implementation • 30 Sep 2023 • Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, Dong Yu
In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on intrinsic changes.
no code implementations • ACL 2018 • Wenlin Yao, Ruihong Huang
Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories.
no code implementations • IJCNLP 2017 • Zeyu Dai, Wenlin Yao, Ruihong Huang
Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts.
no code implementations • RANLP 2017 • Wenlin Yao, Saipravallika Nettyam, Ruihong Huang
Capabilities of detecting temporal relations between two events can benefit many applications.
no code implementations • RANLP 2017 • Wenlin Yao, Zeyu Dai, Ruihong Huang, James Caverlee
The lack of large realistic datasets presents a bottleneck in online deception detection studies.
no code implementations • EMNLP 2020 • Wenlin Yao, Zeyu Dai, Maitreyi Ramaswamy, Bonan Min, Ruihong Huang
We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents.
no code implementations • 28 Oct 2022 • Xiaoman Pan, Wenlin Yao, Hongming Zhang, Dian Yu, Dong Yu, Jianshu Chen
In this paper, we develop a novel semi-parametric language model architecture, Knowledge-in-Context (KiC), which empowers a parametric text-to-text language model with a knowledge-rich external memory.
Ranked #5 on Question Answering on StoryCloze
no code implementations • 8 Jul 2023 • Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, Dong Yu
Specifically, the detection technique achieves a recall of ~88% and the mitigation technique successfully mitigates 57. 6% of the correctly detected hallucinations.
no code implementations • 16 Jul 2023 • Zhenwen Liang, Dian Yu, Xiaoman Pan, Wenlin Yao, Qingkai Zeng, Xiangliang Zhang, Dong Yu
Our approach uniquely considers the various annotation formats as different "views" and leverages them in training the model.
no code implementations • 27 Feb 2024 • Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao, Dong Yu, Tongshuang Wu, Jianshu Chen
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance.