Large Language Models (LLMs) have shown exceptional results on current
benchmarks when working individually. The advancement in their capabilities,
along with a reduction in parameter size and inference times, has facilitated
the use of these models as agents, enabling interactions among multiple models
to execute complex tasks. Such collaborations offer several advantages,
including the use of specialized models (e.g. coding), improved confidence
through multiple computations, and enhanced divergent thinking, leading to more
diverse outputs. Thus, the collaborative use of language models is expected to
grow significantly in the coming years. In this work, we evaluate the behavior
of a network of models collaborating through debate under the influence of an
adversary. We introduce pertinent metrics to assess the adversary's
effectiveness, focusing on system accuracy and model agreement. Our findings
highlight the importance of a model's persuasive ability in influencing others.
Additionally, we explore inference-time methods to generate more compelling
arguments and evaluate the potential of prompt-based mitigation as a defensive
strategy.

评估模型网络在对抗影响下通过辩论进行合作时的行为，探索推理时间方法生成更令人信服的论点，并评估基于提示的缓解作为一种防御策略的潜力。

多智能体协作攻击：通过辩论研究大规模语言模型协作中的对抗攻击

MultiAgent Collaboration Attack: Investigating Adversarial Attacks in  Large Language Model Collaborations via Debate

Despite efforts to expand the knowledge of large language models (LLMs),
knowledge gaps -- missing or outdated information in LLMs -- might always
persist given the evolving nature of knowledge. In this work, we study
approaches to identify LLM knowledge gaps and abstain from answering questions
when knowledge gaps are present. We first adapt existing approaches to model
calibration or adaptation through fine-tuning/prompting and analyze their
ability to abstain from generating low-confidence outputs. Motivated by their
failures in self-reflection and over-reliance on held-out sets, we propose two
novel approaches that are based on model collaboration, i.e., LLMs probing
other LLMs for knowledge gaps, either cooperatively or competitively. Extensive
experiments with three LLMs on four QA tasks featuring diverse knowledge
domains demonstrate that both cooperative and competitive approaches to
unveiling LLM knowledge gaps achieve up to 19.3% improvements on abstain
accuracy against the strongest baseline. Further analysis reveals that our
proposed mechanisms could help identify failure cases in retrieval augmentation
and pinpoint knowledge gaps in multi-hop reasoning.

通过合作和竞争的方式，提出了两种新的方法来识别大型语言模型的知识缺口，并在检索增强和多跳推理中帮助识别失败案例和知识缺口。

勿妄为，自制自己：通过多个 LLM 协作识别 LLM 知识盲点

Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM  Collaboration

Cross-lingual named entity recognition (NER) aims to train an NER system that
generalizes well to a target language by leveraging labeled data in a given
source language. Previous work alleviates the data scarcity problem by
translating source-language labeled data or performing knowledge distillation
on target-language unlabeled data. However, these methods may suffer from label
noise due to the automatic labeling process. In this paper, we propose CoLaDa,
a Collaborative Label Denoising Framework, to address this problem.
Specifically, we first explore a model-collaboration-based denoising scheme
that enables models trained on different data sources to collaboratively
denoise pseudo labels used by each other. We then present an
instance-collaboration-based strategy that considers the label consistency of
each token's neighborhood in the representation space for denoising.
Experiments on different benchmark datasets show that the proposed CoLaDa
achieves superior results compared to previous methods, especially when
generalizing to distant languages.

本文提出了 CoLaDa 协作标签去噪框架来解决跨语言命名实体识别中标签噪声的问题。通过采用不同数据源的模型合作去噪伪标签和基于实例协作的策略来考虑表示空间中邻域的标签一致性，CoLaDa 在不同基准数据集上得到了卓越的结果，特别是在远距离语言的泛化方面。