FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models 论文:arxiv.org/abs/2310.2041仓库:github.com/YJiangcm/Fol 现状 大型语言模型(LLMs)在生成流畅、真实文本方面表现出色,但现实生活中的指令往往要求模型生成的文本不仅要自然,还要符合特定的约束。现有的基准测试主...
然而,对这些能力的评估并不是标准化的:人工评估是昂贵的,缓慢的,而且不能客观地可重复的,而基于LLM的自动评估可能有偏见或受到评估者LLM能力的限制。为了克服这些问题,我们为大型语言模型引入了Instruction-Following Eval (IFEval)。IFEval是一个直接且易于复制的评估基准。它专注于一组“可验证的指令”,如“写出...
Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs often undergo extensive speech instruction-tuning to bridge the gap between speech and text modalities. This requires...
In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of their key-value (KV) caches. Conventional cache management strategies for LLMs focus on cache eviction, which often ...
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel and comprehensive instruction-tuning dataset designed to equip languag...
The Instruction Hierarchy: Training LLMs to Prioritize Privileged InstructionsO网页链接这篇论文提出了“指令层次”概念,旨在训练大型语言模型(LLMs)如GPT-3.5,使其优先执行具有更高优先级的指令,从而抵抗恶意指令的注入和模型被篡改的风险。文章指出,当前LLMs容易受到攻击的原因之一是,模型常常将系统提示(例如应用...
LLaMA-GPT-4 performs similarly to the original GPT-4 in all three criteria, suggesting a promising direction for developing state-of-the-art instruction-following LLMs. Fine-tuning with the data We follow the same reciple to fine-tune LLaMA as Alpaca using standard Hugging Face training code....
Vigogne is a collection of powerful 🇫🇷 French large language models (LLMs) that are open-source and designed for instruction-following and chat purposes. The main contributions of this project include: Open-sourced 🦙 Vigogne models for French instruction-following and chat ...
Why instruction tune LLMs? The utility of instruction tuning, like that of most fine-tuning techniques, lies in the fact that pre-trained LLMs are not optimized for conversations or instruction following. In a literal sense, LLMs do notanswera prompt: they onlyappend text to it.Instruction ...
GraphWiz: An Instruction-Following Language Model for Graph Problems Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph pro... N Chen,Y Li,J Tang,... 被引量: 0发表: 2024年 加载更多0关于...