SALSA: Single-pass Autoregressive LLM Structured Classification
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918172355985408 |
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| author | Berdichevsky, Ruslan Nahum-Gefen, Shai Zaken, Elad Ben |
| author_facet | Berdichevsky, Ruslan Nahum-Gefen, Shai Zaken, Elad Ben |
| contents | Despite their impressive generalization capabilities, instruction-tuned Large Language Models often underperform on text classification benchmarks. We introduce SALSA, a coherent pipeline that combines structured prompting, class-to-token mapping, and parameter-efficient fine-tuning, thereby avoiding cold-start training. Each class label is mapped to a distinct output token, and prompts are constructed to elicit a single-token response. During inference, the model's output is projected only onto the logits of the relevant class tokens, enabling efficient and accurate classification in a single forward pass. SALSA achieves state-of-the-art results across diverse benchmarks, demonstrating its robustness and scalability for LLM-based classification applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_22691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SALSA: Single-pass Autoregressive LLM Structured Classification Berdichevsky, Ruslan Nahum-Gefen, Shai Zaken, Elad Ben Computation and Language Machine Learning Despite their impressive generalization capabilities, instruction-tuned Large Language Models often underperform on text classification benchmarks. We introduce SALSA, a coherent pipeline that combines structured prompting, class-to-token mapping, and parameter-efficient fine-tuning, thereby avoiding cold-start training. Each class label is mapped to a distinct output token, and prompts are constructed to elicit a single-token response. During inference, the model's output is projected only onto the logits of the relevant class tokens, enabling efficient and accurate classification in a single forward pass. SALSA achieves state-of-the-art results across diverse benchmarks, demonstrating its robustness and scalability for LLM-based classification applications. |
| title | SALSA: Single-pass Autoregressive LLM Structured Classification |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2510.22691 |