Papers by Assaf Arbelle
NumeroLogic: Number Encoding for Enhanced LLMs’ Numerical Reasoning (2024.emnlp-main)
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| Challenge: | Language models struggle with numerical and arithmetical tasks, such as multiplying 3-digit numbers. |
| Approach: | They propose a method to include the count of digits before each number instead of “42”. |
| Outcome: | The proposed format improves the reasoning process before generating the actual number. |
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs (2023.emnlp-main)
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Roei Herzig, Alon Mendelson, Leonid Karlinsky, Assaf Arbelle, Rogerio Feris, Trevor Darrell, Amir Globerson
| Challenge: | Vision and language models (VLMs) have demonstrated remarkable zero-shot (ZS) performance in a variety of tasks. |
| Approach: | They propose to integrate structured annotations into visual and textual representations to improve VLMs' understanding of compositional scenes. |
| Outcome: | The proposed method improves VLMs on multiple VL datasets with only a mild degradation in ZS capabilities. |
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. |