9 citations · 18 across the 8 of their papers we have counts for
8 papers
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
Yuxiang Zhang, Xin Fan, Junjie Wang +4
Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. D…
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Kelong Mao, Chenlong Deng, Haonan Chen +4
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization…
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval
Yiyao Yu, Junjie Wang, Yuxiang Zhang +3
Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conver…
Open-Domain Dialogue Quality Evaluation: Deriving Nugget-level Scores from Turn-level Scores
Rikiya Takehi, Akihisa Watanabe, Tetsuya Sakai
Existing dialogue quality evaluation systems can return a score for a given system turn from a particular viewpoint, e.g., engagingness. However, to improve dialogue systems by loc…
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval
Haoxiang Shi, Sumio Fujita, Tetsuya Sakai
Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations an…
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power
Nuo Chen, Tetsuya Sakai
Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for offline evaluation metrics. This framework allows information retrieval (IR) researchers to design evalu…