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Found 11 papers, 7 papers with code
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling
In CRS, implicit patterns of user interest sequence guide the smooth transition of dialog utterances to the goal item.
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target.
CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs
Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths.
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Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Existing benchmarks mainly focus on modeling different types of constraints in human instructions while neglecting the composition of different constraints, which is an indispensable constituent in complex instructions.
ToMBench: Benchmarking Theory of Mind in Large Language Models
Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others.
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks.
CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters.
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach
Experiments verify the superiority of Supporter in achieving positive emotion elicitation during responding while maintaining conversational goals including coherence.
Aligning Recommendation and Conversation via Dual Imitation
Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations.
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Dialogue contradiction is a critical issue in open-domain dialogue systems.
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy.
