Attention-Shifting uses importance-aware suppression on unlearning data and retention enhancement on retained data via dual-loss optimization to achieve selective unlearning with better utility preservation than prior methods.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
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Introduces six-dimension trustworthiness definition and attention-based A-Trust score with a TMS to improve LLM-MAS robustness against malicious or unreliable messages.
ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
citing papers explorer
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Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
Attention-Shifting uses importance-aware suppression on unlearning data and retention enhancement on retained data via dual-loss optimization to achieve selective unlearning with better utility preservation than prior methods.
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To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
Introduces six-dimension trustworthiness definition and attention-based A-Trust score with a TMS to improve LLM-MAS robustness against malicious or unreliable messages.
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Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.