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Chengzhi Li

7 accepted papers

2026

TAMMs: Change Understanding and Forecasting in Satellite Image Time Series with Temporal-Aware Multimodal Models

ICLR 2026poster

Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) are critical, yet historically disjointed tasks in Satellite Image Time Series (SITS) analysis. Both are fundamentally limited by the common challenge of modeling long-range temporal dynamics. To explore how to improve t…

Cited by 0SourceScholar
2026

Understanding Temporal Logic Consistency in Video-Language Models through Cross-Modal Attention Discriminability

CVPR 2026

Large language models (LLMs) often generate self-contradictory outputs, which severely impacts their reliability and hinders their adoption in practical applications. In video-language models (Video-LLMs), this phenomenon recently draws the attention of researchers. Specifically, these models fail t

Cited by 0SourceScholar
2025

Constructing Your Model’s Value Distinction: Towards LLM Alignment with Anchor Words Tuning

EMNLP 2025

With the widespread applications of large language models (LLMs), aligning LLMs with human values has emerged as a critical challenge. For alignment, we always expect LLMs to be honest, positive, harmless, etc. And LLMs appear to be capable of generating the desired outputs after the alignment tunin

2025

Memory or Reasoning? Explore How LLMs Compute Mixed Arithmetic Expressions

ACL 2025finding

Large language models (LLMs) can solve complex multi-step math reasoning problems, but little is known about how these computations are implemented internally. Many recent studies have investigated the mechanisms of LLMs on simple arithmetic tasks (e.g., a+b, a× b), but how LLMs solve mixed arithmet…

Cited by 0SourcePDFScholar
2025

Option Symbol Matters: Investigating and Mitigating Multiple-Choice Option Symbol Bias of Large Language Models

NAACL 2025long

Multiple-Choice Question Answering (MCQA) is a widely used task in the evaluation of Large Language Models (LLMs). In this work, we reveal that current LLMs’ performance in MCQA could be heavily influenced by the choice of option symbol sets, due to the option symbol bias. That is, when altering onl…

Cited by 0SourcePDFScholar
2016

Connectivity for overlaid wireless networks with outage constraints

ICASSP 2016accepted

We study the connectivity of overlaid wireless networks where two users can communicate if the signal-to-interference ratio is larger than a threshold subject to an outage constraint. By using percolation theory, we first specify a 2-dimensional connectivity region defined as the set of density pair…

Cited by 0SourceScholar