Search Results for author: Jiashuo Wang

Found 13 papers, 9 papers with code

MIO: A Foundation Model on Multimodal Tokens

1 code implementation26 Sep 2024 Zekun Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jiashuo Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.

model Text Generation

Interpretable Differential Diagnosis with Dual-Inference Large Language Models

no code implementations10 Jul 2024 Shuang Zhou, Mingquan Lin, Sirui Ding, Jiashuo Wang, Genevieve B. Melton, James Zou, Rui Zhang

To the best of our knowledge, it is the first work that customizes LLMs for DDx explanation and comprehensively evaluates their interpretation performance.

Decision Making

Towards a Client-Centered Assessment of LLM Therapists by Client Simulation

no code implementations18 Jun 2024 Jiashuo Wang, Yang Xiao, Yanran Li, Changhe Song, Chunpu Xu, Chenhao Tan, Wenjie Li

To this end, we adopt LLMs to simulate clients and propose ClientCAST, a client-centered approach to assessing LLM therapists by client simulation.

Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue

1 code implementation10 Feb 2024 Jian Wang, Chak Tou Leong, Jiashuo Wang, Dongding Lin, Wenjie Li, Xiao-Yong Wei

Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents.

Dialogue Generation

Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback

1 code implementation11 Jan 2024 Jiashuo Wang, Chunpu Xu, Chak Tou Leong, Wenjie Li, Jing Li

An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges.

Contrastive Learning

How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation

2 code implementations28 Dec 2023 Yang Xiao, Yi Cheng, Jinlan Fu, Jiashuo Wang, Wenjie Li, PengFei Liu

In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs).

AI Agent Language Modelling

Self-Detoxifying Language Models via Toxification Reversal

2 code implementations14 Oct 2023 Chak Tou Leong, Yi Cheng, Jiashuo Wang, Jian Wang, Wenjie Li

Drawing on this idea, we devise a method to identify the toxification direction from the normal generation process to the one prompted with the negative prefix, and then steer the generation to the reversed direction by manipulating the information movement within the attention layers.

Language Modeling Language Modelling

Aligning Language Models with Human Preferences via a Bayesian Approach

1 code implementation NeurIPS 2023 Jiashuo Wang, Haozhao Wang, Shichao Sun, Wenjie Li

For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans.

Contrastive Learning Reinforcement Learning (RL) +1

CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation

1 code implementation1 Nov 2022 Jiashuo Wang, Yi Cheng, Wenjie Li

Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user's feelings and experiences.

Decoder Empathetic Response Generation +2

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

1 code implementation9 Oct 2022 Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions.

Dialogue Generation

Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation

1 code implementation26 Sep 2022 Peiqin Lin, Jiashuo Wang, Hinrich Schütze, Wenjie Li

To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i. e., what personal experiences are described) and the emotion view (i. e., the feelings of the speaker on these experiences).

Disentanglement Empathetic Response Generation +1

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