Papers by Ohad Amosy
LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals (2026.findings-acl)
Copied to clipboard
| Challenge: | Concept-based explanations quantify how high-level concepts influence model behavior . existing benchmarks rely on costly human-written counterfactuals that serves as imperfect proxy . |
| Approach: | They propose a framework for constructing datasets containing structural counterfactual pairs . they use a structured Causal Model to generate a concept-based explanation . |
| Outcome: | The proposed framework compares concept-based explanations to causal effects estimated from counterfactuals. |
Text2Model: Text-based Model Induction for Zero-shot Image Classification (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot learning are limited in two ways: Query-dependence and richness of language description. |
| Approach: | They propose a task-agnostic approach to image classification using only text descriptions . they train a hypernetwork that receives class descriptions and outputs a multi-class model . |
| Outcome: | The proposed approach generates non-linear classifiers, handles rich textual descriptions, and may be adapted to produce lightweight models efficient enough for on-device applications. |
Example-based Hypernetworks for Multi-source Adaptation to Unseen Domains (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In order to achieve unprecedented performance, many out-of-distribution generalization approaches use unlabeled data from the target distribution. |
| Approach: | They propose a framework that leverages labeled data from multiple source domains to generalize to unknown target domains at training. |
| Outcome: | The proposed framework outperforms existing models in two tasks, and it is compared to few-shot GPT-3. |