Papers with HARM
HARM: Learning Hate-Aware Reward Model for Evaluating Natural Language Explanations of Offensive Content (2026.findings-eacl)
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| Challenge: | Existing reward models for explaining hate speech are optimized for broad notions of safety, but they assign lower scores to contextually rich explanations. |
| Approach: | They propose a reward model that integrates interpretable signals to better align reward scores with the needs of hate speech explanation. |
| Outcome: | The proposed model outperforms general-purpose baselines and improves pair-wise preference. |
AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation (2025.findings-naacl)
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| Challenge: | Assertions have been the de facto collateral for hardware for over a decade. |
| Approach: | They propose a benchmark to evaluate LLMs’ effectiveness for assertion generation quantitatively. |
| Outcome: | The proposed benchmark compares state-of-the-art LLMs with existing benchmarks and shows that they generate higher fractions of functionally correct assertions. |
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (2024.emnlp-main)
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| Challenge: | Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage. |
| Approach: | They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy. |
| Outcome: | The proposed approach scales up the diversity of test cases using a top-down approach based on an extensible, fine-grained risk taxonomy and leverages reinforcement learning techniques to facilitate multi-turn adversarial probing in a human-like manner. |