Papers with Fine-tuned models
eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables (2025.naacl-industry)
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| Challenge: | eC-Tab2Text dataset is designed to capture product attributes and user-specific queries. |
| Approach: | They propose a novel dataset to capture the intricacies of e-commerce including detailed product attributes and user-specific queries. |
| Outcome: | The proposed dataset outperforms existing generalpurpose LLMs in generating accurate product reviews. |
Pathologies of Neural Models Make Interpretations Difficult (D18-1)
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| Challenge: | Existing methods for NLP use input reduction to determine a word's importance . human accuracy degrades when shown the reduced examples instead of the original . |
| Approach: | They propose a process that iteratively removes the least important word from an input . they show human models make the same predictions with high confidence . |
| Outcome: | The proposed methods expose pathological behaviors of neural models . human experiments show that reduced examples lack information to support the prediction of any label . |
Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment. |
| Approach: | They propose a framework that trains models to engage in explicit safe reasoning before response . they propose RATIONAL, which allows models to reject harmful prompts while providing meaningful and context-aware responses. |
| Outcome: | The proposed framework fine-tunes models to reason about query intent, ethics, and potential harm. |