Large Language Models Are Not Robust Multiple Choice Selectors Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, Minlie Huang 2023 PiCO: Peer Review in LLMs based on the Consistency Optimization Kun-Peng Ning, Shuo Yang, Yu-Yang Liu, ...
Large Language Models Are Not Robust Multiple Choice Selectors Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, Minlie Huang 2023 SpeechVerse: A Large-scale Generalizable Audio Language Model Nilaksh Das, Saket Dingliwal, S. Ronanki, Rohit Paturi,...
Indeed, the research showed that these models are not as robust as many initially think. Theirhigh performanceis limited to common task variants and suffer from consistent and severe performance drop in the unfamiliar counterfactual scenarios, indicating a lack of generalizable addition ability. The pa...
切换模式 登录/注册 赵泠 2023 年度新知答主 —Rodney Brooks, Robust.AI 发布于 2023-05-24 11:27・IP 属地中国香港 赞同 134 分享 收藏 写下你的评论... 23 条评论 默认 最新 飞云希望 主要问题是人呢?人的大部分语言表达是“应然”还是“实然”?在大量的should sound like下,是否...
ensuring that identified causal links are robust and not merely correlational. 3. Context-Aware Prompting Mechanism (CAPM): CAPM crafts tailored prompts that encapsulate enriched context and counterfactual insights, directing Large Language Models toward more precise and accurate causal reasoning. CARE-CA...
You can improve this by robust error handling, fine-tuning domain-specific data, post-processing help and identifying and filtering out glitch tokens. Types of large language models Transformer-based model: These models, such as GPT, BERT (Bidirectional Encoder Representations from Transformers),...
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Fine-Tuning Large Language Models (LLMs) with Shawhin Talebi, Medium, 2023 Large Language Diffusion Models relevant repos: online videos and blogs Resource on LLM visualization The resource below attempts to visualize what is happening in LLM under the hood and is a helpful tool to comprehend th...
[论文翻译]Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection 鬼火少女布狼牙 关注洛天依喵谢谢喵4 人赞同了该文章 目录 收起 摘要 引言 相关工作 预备知识 方法 实验 结果与讨论 消融实验 结论 摘要 通过利用像 CLIP 这样强大的视觉语言模型(VLM),分布外(OOD)...
aggressive filtering leads to performance degradation on a wide range of tasks for GPT-like LLMs (since filtering objectives 不够robust) solution: Marion et al. 提出的three data quality estimators: (perplexity + Error L2-Norm(EL2N), memorization) ...