Papers with open-weight model

3 papers
Literary Evidence Retrieval via Long-Context Language Models (2025.acl-short)

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Challenge: a recent study shows that long-context language models can exceed human expert performance in literary analysis . despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration.
Approach: They propose a task where a model is given an entire text of a book and a literary criticism with a missing quotation from that work and asked to generate the missing quote.
Outcome: The proposed model outperforms open-weight models in literary evidence retrieval tasks.
SGT: Securing Open-Source LLMs Against Malicious Fine-tuning via Safety Guidance Trigger (2026.acl-long)

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Challenge: open-weight large language models increase vulnerability to malicious fine-tuning . despite these advantages, open-source LLMs increase the potential for misuse .
Approach: They propose a safety guide for open-weight large language models that guides fine-tuning toward the safety manifold to preserve alignment.
Outcome: The proposed safety guidance trigger significantly improves robustness against malicious fine-tuning.
PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian (2025.naacl-long)

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Challenge: Large language models predominantly reflect Western cultures due to the dominance of English-centric training data.
Approach: They propose a dataset to assess the sensitivity of LLMs to Persian culture.
Outcome: The proposed model shows a 11.3% gap between best closed-source model and layperson baseline while the gap increases to 21.3% by using the best open-weight model.

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