23 citations · 65 across the 24 of their papers we have counts for
24 papers
ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog
Lingao Xiao, Yalun Dai, Yangyu Huang +17
Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multi…
ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
Qihao Zhao, Yangyu Huang, Yalun Dai +8
Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must groun…
Demystifying Data Organization for Enhanced LLM Training
Yalun Dai, Yangyu Huang, Tongshen Yang +8
Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely s…
GENIUS: Generative Fluid Intelligence Evaluation Suite
Ruichuan An, Sihan Yang, Ziyu Guo +8
Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess , which reli…
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
Huanyu Zhang, Wenshan Wu, Chengzu Li +9
While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…
DocReward: A Document Reward Model for Structuring and Stylizing
Junpeng Liu, Yuzhong Zhao, Bowen Cao +17
Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic professionalism, which is equally cri…
11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
Chengzu Li, Wenshan Wu, Huanyu Zhang +6
For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…
Data Efficacy for Language Model Training
Yalun Dai, Yangyu Huang, Xin Zhang +6
Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or opt…
Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
Huanyu Zhang, Chengzu Li, Wenshan Wu +8
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks. However, recent studies have exposed critical limitations in the…
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Chengzu Li, Wenshan Wu, Huanyu Zhang +5
Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it s…
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning
Yadong Zhang, Shaoguang Mao, Wenshan Wu +4
This paper introduces BI-Directional DEliberation Reasoning (BIDDER), a novel reasoning approach to enhance the decision rationality of language models. Traditional reasoning metho…
Meta Reasoning for Large Language Models
Peizhong Gao, Ao Xie, Shaoguang Mao +4
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) inspired by human meta-reasoning. Traditional in-context…
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Wenshan Wu, Shaoguang Mao, Yadong Zhang +4
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial a…
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
Yadong Zhang, Shaoguang Mao, Tao Ge +7
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that n…
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
Shaoguang Mao, Yuzhe Cai, Yan Xia +5
This paper introduces Alympics (Olympics for Agents), a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a ver…
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation
Wang You, Wenshan Wu, Yaobo Liang +8
Plan-and-Write is a common hierarchical approach in long-form narrative text generation, which first creates a plan to guide the narrative writing. Following this approach, several…
Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
Zhenhailong Wang, Shaoguang Mao, Wenshan Wu +3
Human intelligence thrives on cognitive synergy, where collaboration among different minds yield superior outcomes compared to isolated individuals. In this work, we propose Solo P…
Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models
Zhiyi Wang, Shaoguang Mao, Wenshan Wu +3
This work introduces approaches to assessing phrase breaks in ESL learners' speech using pre-trained language models (PLMs) and large language models (LLMs). There are two tasks: o…
Smart Word Suggestions for Writing Assistance
Chenshuo Wang, Shaoguang Mao, Tao Ge +5
Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces "Smart Word Suggestions" (SWS) task and benchmark.…
Learning to Plan with Natural Language
Yiduo Guo, Yaobo Liang, Chenfei Wu +3
Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide…
Low-code LLM: Graphical User Interface over Large Language Models
Yuzhe Cai, Shaoguang Mao, Wenshan Wu +10
Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a nov…
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Yaobo Liang, Chenfei Wu, Ting Song +11
Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning an…
Assessing Phrase Break of ESL speech with Pre-trained Language Models
Zhiyi Wang, Shaoguang Mao, Wenshan Wu +1
This work introduces an approach to assessing phrase break in ESL learners' speech with pre-trained language models (PLMs). Different with traditional methods, this proposal conver…
An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings
Wenxuan Ye, Shaoguang Mao, Frank Soong +4
Many mispronunciation detection and diagnosis (MD&D) research approaches try to exploit both the acoustic and linguistic features as input. Yet the improvement of the performance i…