3 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View
Yongjie Xiao, Hongru Liang, Peixin Qin +2
Despite the great potential of large language models(LLMs) in machine comprehension, it is still disturbing to fully count on them in real-world scenarios. This is probably because…
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
Peixin Qin, Chen Huang, Yang Deng +2
With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs…
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models
Tong Zhang, Peixin Qin, Yang Deng +6
Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity rema…
Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors
Chen Huang, Peixin Qin, Yang Deng +3
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.…
Towards Equipping Transformer with the Ability of Systematic Compositionality
Chen Huang, Peixin Qin, Wenqiang Lei +1
One of the key factors in language productivity and human cognition is the ability of systematic compositionality, which refers to understanding composed unseen examples of seen pr…