1 citations · 2 across the 13 of their papers we have counts for
14 papers
RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems
Haichuan Hu, Yang Xiao, Mingni Tang +7
Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-p…
Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics
Jiashuo Wang, Fenggang Yu, Jian Wang +6
Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strategic use of language to evoke…
Foresight Optimization for Strategic Reasoning in Large Language Models
Jiashuo Wang, Jiawen Duan, Jian Wang +6
Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-based LLMs to perform effectiv…
SCALE: Selective Resource Allocation for Overcoming Performance Bottlenecks in Mathematical Test-time Scaling
Yang Xiao, Chunpu Xu, Ruifeng Yuan +3
Test-time compute scaling has emerged as a powerful paradigm for enhancing mathematical reasoning in large language models (LLMs) by allocating additional computational resources d…
Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments
Jiashuo Wang, Kaitao Song, Chunpu Xu +5
Enhancing user engagement through interactions plays an essential role in socially-driven dialogues. While prior works have optimized models to reason over relevant knowledge or pl…
Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
Yang Xiao, Jiashuo Wang, Qiancheng Xu +5
As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic…
SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution
Hanlin Wang, Chak Tou Leong, Jiashuo Wang +2
Reinforcement learning (RL) holds significant promise for training LLM agents to handle complex, goal-oriented tasks that require multi-step interactions with external environments…
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling
Yang Xiao, Jiashuo Wang, Ruifeng Yuan +4
Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data…
Towards a Client-Centered Assessment of LLM Therapists by Client Simulation
Jiashuo Wang, Yang Xiao, Yanran Li +4
Although there is a growing belief that LLMs can be used as therapists, exploring LLMs' capabilities and inefficacy, particularly from the client's perspective, is limited. This wo…
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
Jian Wang, Chak Tou Leong, Jiashuo Wang +3
Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as res…
Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback
Jiashuo Wang, Chunpu Xu, Chak Tou Leong +2
An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges. To generate supportive responses, it is critica…
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Yang Xiao, Yi Cheng, Jinlan Fu +3
In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the la…
Self-Detoxifying Language Models via Toxification Reversal
Chak Tou Leong, Yi Cheng, Jiashuo Wang +2
Language model detoxification aims to minimize the risk of generating offensive or harmful content in pretrained language models (PLMs) for safer deployment. Existing methods can b…
Aligning Language Models with Human Preferences via a Bayesian Approach
Jiashuo Wang, Haozhao Wang, Shichao Sun +1
In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current…