Counterfactual examples are useful for exploring the decision boundaries of
machine learning models and determining feature attributions. How can we apply
counterfactual-based methods to analyze and explain LLMs? We identify the
following key challenges. First, the generated textual counterfactuals should
be meaningful and readable to users and thus can be mentally compared to draw
conclusions. Second, to make the solution scalable to long-form text, users
should be equipped with tools to create batches of counterfactuals from
perturbations at various granularity levels and interactively analyze the
results. In this paper, we tackle the above challenges and contribute 1) a
novel algorithm for generating batches of complete and meaningful textual
counterfactuals by removing and replacing text segments in different
granularities, and 2) LLM Analyzer, an interactive visualization tool to help
users understand an LLM's behaviors by interactively inspecting and aggregating
meaningful counterfactuals. We evaluate the proposed algorithm by the
grammatical correctness of its generated counterfactuals using 1,000 samples
from medical, legal, finance, education, and news datasets. In our experiments,
97.2% of the counterfactuals are grammatically correct. Through a use case,
user studies, and feedback from experts, we demonstrate the usefulness and
usability of the proposed interactive visualization tool.

生成完整且有意义的文本反事实示例的新算法以及交互式可视化工具，用于分析和解释 LLMs。

使用有意义对照实验的交互式 LLMs 分析

Interactive Analysis of LLMs using Meaningful Counterfactuals

This year, the Nara Institute of Science and Technology (NAIST)'s submission
to the 2015 Workshop on Asian Translation was based on syntax-based statistical
machine translation, with the addition of a reranking component using neural
attentional machine translation models. Experiments re-confirmed results from
previous work stating that neural MT reranking provides a large gain in
objective evaluation measures such as BLEU, and also confirmed for the first
time that these results also carry over to manual evaluation. We further
perform a detailed analysis of reasons for this increase, finding that the main
contributions of the neural models lie in improvement of the grammatical
correctness of the output, as opposed to improvements in lexical choice of
content words.

研究表明，使用基于语法的统计机器翻译加上使用神经注意力机器翻译模型的重新排序组件进行机器翻译的效果较好，尤其对于语法正确性方面有较大提升。