Papers by Zhiqiu Lin
Activation Reward Models for Few-Shot Model Alignment (2026.findings-acl)
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Tianning Chai, Chancharik Mitra, Brandon Huang, Gautam Rajendrakumar Gare, Zhiqiu Lin, Assaf Arbelle, Leonid Karlinsky, Rogerio Feris, Trevor Darrell, Deva Ramanan, Roei Herzig
| Challenge: | A common approach is to use reward models that enable reinforcement-learning post-training. |
| Approach: | They propose a method that steers LLM activations to align with few-shot preference data without finetuning. |
| Outcome: | The proposed method surpasses zero-shot, few-shot and voting-based benchmarks on reward hacking and noise signals. |
Prompting Scientific Names for Zero-Shot Species Recognition (2023.emnlp-main)
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| Challenge: | We use visionlanguage models (VLMs) to recognize images of common objects in a zero-shot fashion, but it is underexplored how to use CLIP for zero- shot species recognition of highly specialized concepts. |
| Approach: | They propose a method to translate scientific names to common English names and use them in prompts to improve their performance. |
| Outcome: | The proposed method performs poorly for species recognition with prompts that use scientific names, e.g., “a photo of Lepus Timidus” (which is a scientific name in Latin) and additionally use them in the prompts. |
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning (2025.acl-long)
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Zifu Wan, Yaqi Xie, Ce Zhang, Zhiqiu Lin, Zihan Wang, Simon Stepputtis, Deva Ramanan, Katia P. Sycara
| Challenge: | Large multimodal foundation models perceive objects as indivisible, overlooking the components that constitute them. |
| Approach: | They propose a novel benchmark for large multimodal foundation models comprising hand-labeled part segmentation annotations and task-oriented instructions to evaluate their performance. |
| Outcome: | The proposed benchmark improves performance of current models in understanding and executing part-level tasks within everyday contexts. |