44 citations · 143 across the 30 of their papers we have counts for
32 papers
Trust-Region Adaptive Policy Optimization
Mingyu Su, Jian Guan, Yuxian Gu +2
Post-training methods, especially Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), play an important role in improving large language models' (LLMs) complex reasoning…
Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
Chuanqi Cheng, Jian Guan, Wei Wu +1
Long-form video processing fundamentally challenges vision-language models (VLMs) due to the high computational costs of handling extended temporal sequences. Existing token prunin…
PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models
Xueliang Zhao, Wei Wu, Jian Guan +1
The ability of large language models to solve complex mathematical problems has progressed significantly, particularly for tasks requiring advanced reasoning. However, the scarcity…
A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
Jian Guan, Junfei Wu, Jia-Nan Li +2
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to ind…
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment
Jia-Nan Li, Jian Guan, Songhao Wu +2
Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in…
Theoretical Benefit and Limitation of Diffusion Language Model
Guhao Feng, Yihan Geng, Jian Guan +3
Diffusion language models have emerged as a promising approach for text generation. One would naturally expect this method to be an efficient replacement for autoregressive models…
Human Decision-making is Susceptible to AI-driven Manipulation
Sahand Sabour, June M. Liu, Siyang Liu +13
AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation…
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models
Jia-Nan Li, Jian Guan, Wei Wu +2
Tables are ubiquitous across various domains for concisely representing structured information. Empowering large language models (LLMs) to reason over tabular data represents an ac…
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
Jiaxin Wen, Jian Guan, Hongning Wang +2
Despite the remarkable success of large language models (LLMs) on traditional natural language processing tasks, their planning ability remains a critical bottleneck in tackling co…
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules
Zhuocheng Gong, Ang Lv, Jian Guan +6
Is it always necessary to compute tokens from shallow to deep layers in Transformers? The continued success of vanilla Transformers and their variants suggests an undoubted "yes".…
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
Chuanqi Cheng, Jian Guan, Wei Wu +1
We explore multi-step reasoning in vision-language models (VLMs). The problem is challenging, as reasoning data consisting of multiple steps of visual and language processing are b…
AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback
Jian Guan, Wei Wu, Zujie Wen +3
The notable success of large language models (LLMs) has sparked an upsurge in building language agents to complete various complex tasks. We present AMOR, an agent framework based…
Language Models Hallucinate, but May Excel at Fact Verification
Jian Guan, Jesse Dodge, David Wadden +2
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently "hallucinate," resulting in non…
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation
Jian Guan, Minlie Huang
Despite the huge progress in myriad generation tasks, pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts with maximization-based decoding algorit…
ReDial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training
Jiaxin Wen, Hao Zhou, Jian Guan +1
Pre-training on large-scale open-domain dialogue data can substantially improve the performance of dialogue models. However, the pre-trained dialogue model's ability to utilize lon…
A Benchmark for Understanding and Generating Dialogue between Characters in Stories
Jianzhu Yao, Ziqi Liu, Jian Guan +1
Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can u…
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing
Xuekai Zhu, Jian Guan, Minlie Huang +1
Non-parallel text style transfer is an important task in natural language generation. However, previous studies concentrate on the token or sentence level, such as sentence sentime…
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Jian Guan, Zhenyu Yang, Rongsheng Zhang +2
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories a…
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Zhexin Zhang, Jiaxin Wen, Jian Guan +1
Endowing the protagonist with a specific personality is essential for writing an engaging story. In this paper, we aim to control the protagonist's persona in story generation, i.e…
A Corpus for Understanding and Generating Moral Stories
Jian Guan, Ziqi Liu, Minlie Huang
Teaching morals is one of the most important purposes of storytelling. An essential ability for understanding and writing moral stories is bridging story plots and implied morals.…
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
Yuan Yao, Qingxiu Dong, Jian Guan +32
Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Jian Guan, Zhuoer Feng, Yamei Chen +4
Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing…
CPM-2: Large-scale Cost-effective Pre-trained Language Models
Zhengyan Zhang, Yuxian Gu, Xu Han +16
In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-…
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
Jian Guan, Xiaoxi Mao, Changjie Fan +3
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in model…