Papers by Rimon Melamed
Demystifying optimized prompts in language models (2025.emnlp-main)
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| Challenge: | Modern language models (LMs) are not robust to out-of-distribution inputs. |
| Approach: | They investigate the composition of machine generated (“optimized”) prompts and the mechanisms by which LMs parse and build predictions from them. |
| Outcome: | The proposed prompts are primarily composed of punctuation and noun tokens, which are more rare in the training data. |
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users (2024.lrec-main)
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Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay K. Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
| Challenge: | Current approaches focus on improving ranking performance at the cost of escalating complexity and complicating the task. |
| Approach: | They propose a hybrid multi-task learning approach that trains on user-item and item-i item interactions. |
| Outcome: | The proposed approach improves accuracy, relevance, and diversity of user recommendations even for cold-start users. |
Prompts have evil twins (2024.emnlp-main)
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| Challenge: | We find that many natural-language prompts can be replaced by corresponding unintelligible prompts that provably elicit similar behavior in language models. |
| Approach: | They find that natural-language prompts can be replaced by corresponding unintelligible prompts that elicit similar behavior in language models. |
| Outcome: | The proposed prompts are obfuscated and uninterpretable but mimic the original natural-language prompts . the problem has applications of independent interest, the authors argue . |