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 Large Language Model





Context-Free LLM Approximation for Guiding Program Synthesis Shraddha Barke UC San Diego San Diego, USA

Neural Information Processing Systems

Our Solution: Context-Free LLM Approximation To bridge this gap and harness the power of LLMs to guide bottom-up search, we propose to approximate the LLM's conditional output


Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning Hao Ma

Neural Information Processing Systems

Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs.




Entity Alignment with Noisy Annotations from Large Language Models

Neural Information Processing Systems

However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels that may mislead the alignment.



'A famous victory' - South Africa stun India after De Klerk's heroics

BBC News

This content is not available in your location. Nadine de Klerk hits 84 off 54 balls as South Africa recover from 81-5 to chase down their target of 252 with seven balls to spare, securing a famous three wicket win against hosts India at the ICC Women's Cricket World Cup. 'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history. Video, 00:04:27 'I was asking ChatGPT is this real?' - Fraser & Tulloch on making black history'We've got mountains to do' - Cavallo on homophobia in football. Video, 00:01:58 'We've got mountains to do' - Cavallo on homophobia in football We have already lost too many games - Mahomes.