Bloomberg Data Science Fellow @binwu_cs presents "A Joint Optimization Framework for Enhancing Efficiency of Tool Utilization in LLM Agents" in today's #ACL2025NLP's Session 12: IP Posters (Findings Posters 4) session (11:00-12:30 CEST)
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Thanks Sebastian for sharing our work (arxiv.org/abs/2405.14394). For instruction tuning, masking user prompts is generally beneficial. However, if instruction tuning data is limited and completions are short, including the prompt loss during training might be advantageous.
Thanks Sebastian for sharing our work (arxiv.org/abs/2405.14394). For instruction tuning, masking user prompts is generally beneficial. However, if instruction tuning data is limited and completions are short, including the prompt loss during training might be advantageous.
@abacaj@rasbt@HamelHusain@ashutoshmehra@dan_s_becker We show that in many scenarios, applying loss to instructions could largely improve the performance of instruction tuning on various NLP and open-ended generation tasks. In the most advantageous case, its boosts AlpacaEval 1.0 performance by over 100%.
arxiv.org/abs/2405.14394
This Friday, our PhD Student, @ZhengxiangShi @ucl_wi_group will give a talk entitled "Aligning Language Models with Downstream Tasks: Insights from a Language Modeling Perspective" based on his recent publication to Neurips 2023. Registration [tinyurl.com/uclwitalk-7]. #UCL
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