6 citations · 21 across the 17 of their papers we have counts for
17 papers
LLM2: Let Large Language Models Harness System 2 Reasoning
Cheng Yang, Chufan Shi, Siheng Li +3
Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are r…
Large Language Models Can Self-Improve in Long-context Reasoning
Siheng Li, Cheng Yang, Zesen Cheng +4
Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. Existing approaches typically involve fi…
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Zicheng Lin, Tian Liang, Jiahao Xu +7
Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we int…
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Cheng Yang, Guoping Huang, Mo Yu +6
Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neura…
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Deng Cai, Huayang Li, Tingchen Fu +11
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstra…
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Cheng Yang, Chufan Shi, Yaxin Liu +11
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-i…
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Chufan Shi, Cheng Yang, Xinyu Zhu +6
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activat…
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
Huayang Li, Siheng Li, Deng Cai +5
Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle variou…
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
Siheng Li, Cheng Yang, Yichun Yin +6
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stym…
NewsDialogues: Towards Proactive News Grounded Conversation
Siheng Li, Yichun Yin, Cheng Yang +7
Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce…
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Zesen Cheng, Peng Jin, Hao Li +5
The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by…
Question Answering as Programming for Solving Time-Sensitive Questions
Xinyu Zhu, Cheng Yang, Bei Chen +3
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature o…
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Taiqiang Wu, Xingyu Bai, Weigang Guo +3
Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods…
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Zesen Cheng, Pengchong Qiao, Kehan Li +6
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendo…
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
Wangjie Jiang, Zhihao Ye, Zijing Ou +7
Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensi…
EmpHi: Generating Empathetic Responses with Human-like Intents
Mao Yan Chen, Siheng Li, Yujiu Yang
In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathet…