Papers by Jonathan Richard Schwarz
Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses (2025.emnlp-industry)
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| Challenge: | DeCE is model-agnostic and domain-general, requiring no predefined taxonomies or handcrafted rubrics. |
| Approach: | They propose a decomposed LLM evaluation framework that separates accuracy and recall from accuracy and relevance. |
| Outcome: | The proposed framework achieves stronger correlation with expert judgments than traditional metrics and pointwise LLM scoring. |
Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts (2025.acl-long)
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| Challenge: | Sparse Interpolated Mixture-of-Experts (SIMoE) instruction-tuning is an end-to-end algorithm designed to fine-tune a dense pre-trained Large Language Model (LLM) into a MoE-style model that possesses capabilities in multiple specialized domains. |
| Approach: | They propose an algorithm to fine-tune a dense pre-trained Large Language Model into a MoE-style model that possesses capabilities in multiple specialized domains. |
| Outcome: | The proposed algorithm achieves state-of-the-art on common instruction-tuning benchmarks while maintaining an optimal performance-compute trade-off compared to baselines. |