155 citations · 177 across the 12 of their papers we have counts for
13 papers
RWGBench: Evaluating Scholarly Positioning in Related Work Generation
Anzhe Xie, Weihang Su, Jiaxin Mao +4
Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited. Existing RWG evaluations largely inherit…
SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation
Weihang Su, Anzhe Xie, Qingyao Ai +5
The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show promise for automating this proces…
Evaluating Intelligence via Trial and Error
Jingtao Zhan, Jiahao Zhao, Jiayu Li +7
Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework…
Prompt Refinement with Image Pivot for Text-to-Image Generation
Jingtao Zhan, Qingyao Ai, Yiqun Liu +5
For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experienc…
Scaling Laws For Dense Retrieval
Yan Fang, Jingtao Zhan, Qingyao Ai +4
Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neu…
An Intent Taxonomy of Legal Case Retrieval
Yunqiu Shao, Haitao Li, Yueyue Wu +5
Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' inform…
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Haitao Li, Qingyao Ai, Jingtao Zhan +4
Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness…
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
Jingtao Zhan, Qingyao Ai, Yiqun Liu +4
Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can enc…
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
Wenqiang Lei, Yao Zhang, Feifan Song +5
Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner…
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
Jingtao Zhan, Xiaohui Xie, Jiaxin Mao +4
A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models ha…
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei +6
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This is…
Interpreting Dense Retrieval as Mixture of Topics
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we…
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
Zeyang Liu, Ke Zhou, Jiaxin Mao +1
Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate…