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\def\easy{\emph{w/ cues }}
\def\hard{\emph{w/o cues }}

%%%%\def\overlap{\emph{lexical overlap}}
%%%%\def\subs{\emph{subsequence}}
%%%%\def\const{\emph{constituent}}

%%%%\newcommand\copa[3]{
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%%%%    \item \textbf{Premise}: #1
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\Volume{29}
\Number{3}
\Month{September}
\Year{2022}
%%%%\Issuetitle{Special Issue: XXXXXXXX}



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%%%%   Title, Author, etc.            %%%
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\esubject{Society Column}
\etitle{Are Prompt-based Models Clueless?}
\eauthor{Pride Kavumba\affiref{Author_1}\affiref{Author_2} \and Ryo Takahashi\affiref{Author_3} \and Yusuke Oda\affiref{Author_3}\affiref{Author_1}} 

\headauthor{Kavumba et al.}
\headtitle{Are Prompt-based Models Clueless?}

\affilabel{Author_1}{}{Tohoku University}
\affilabel{Author_2}{}{RIKEN AIP}
\affilabel{Author_3}{}{LegalForce Research}


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\begin{document}
\maketitle


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\section{Introduction}

Pretrained language models have achieved remarkable performance on most of the natural language benchmarks.
Until recently, the dominant approach of adapting these models to downstream tasks has been through finetuning a task-specific head.
But, previous work has found that these models learn to exploit spurious correlations between inputs and the labels \cite{gururangan-etal-2018-annotation,poliak-etal-2018-hypothesis,kavumba-etal-2019-choosing}.
These spurious correlations may exist in the form of unique input tokens or style or annotation artifacts, which all fall under the wide umbrella of \emph{superficial cues} \cite{kavumba-etal-2019-choosing}.
While superficial cues are strong predictors of the labels, they have nothing to do with the intended task.
In software development, superficial cues can be viewed as bugs in the task design.
They enable the task to be solved in non-intended ways, hence the dataset becomes a poor benchmark of the required ability or abilities.
In causality, we can view superficial cues as confounding variables.
Unfortunately, superficial cues tend to be unique to a single dataset.
Thus, the model's remarkable performance on one dataset does not transfer to other datasets, i.e., the models tend not to be robust.
In order to develop models that are truly  robust, we need to identify and examine if the models are exploiting superficial cues.
This is the first step to debug both the dataset and the model's.
Only then can we train robust models that learn to be right for the right reasons \cite{kavumba-etal-2021-learning}.


Recently, prompting has been shown to achieve competitive performance to models finetuned using a task-specific head.
This is despite using a few task-specific finetuning examples.
Given the little data that prompt-based models require to achieve optimal performance on downstream tasks, do they also learn to exploit superficial cues?
Thus, we asked, Do prompt-based models exploit superficial cues?
This is the research question explored in \citeA{kavumba-etal-2022-prompt}.
We presented a rigorous empirical investigation of prompt-based models and whether or not they also exploit superficial cues.
We examined prompted-based models on two English language fundamental tasks of natural language understanding: natural language inference (NLI) and commonsense reasoning;
comprehending natural language inference and commonsense are essential to make progress in natural language understanding.
We then analyzed the performance of prompt-based models---trained on SNLI \cite{bowman-etal-2015-large-snli}, MNLI \cite{williams-etal-2018-broad}, and COPA \cite{roemmele2011choice}---on instances with and without superficial cues.


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\section{Foundation: Debugging Datasets for Superficial Cues}

Superficial cues, exist in all sorts of form and shape.
To facilitate rigorous analysis, we defined two types of superficial cues that abstract away from the underlying tasks: \emph{context} and \emph{contextless} superficial cues.
This allows us to flexibly define the \emph{context} based on the task.
For example, in natural language inference tasks, we define the premise as the \emph{context}, while in multiple-choice tasks, we define the question as the \emph{context}.
Context superficial cues refer to superficial cues that coexist in the context (e.g.~premise) and the hypothesis. For example, lexical overlap.
That is, context superficial cues are only exploitable when the full input is present.
In contrast, contextless superficial cues exist only in the hypothesis (in NLI) or in answer choices (in multiple-choice tasks).
A dataset can contain either one or both types of superficial cues.
Therefore, both types must be investigated to sufficiently answer whether a model can exploit superficial cues.

Equipped with these definitions, we reanalyzed superficial cues in MNLI, SNLI, and COPA datasets to create evaluation sets that have and do not have superficial cues.
We found that these datasets contain more superficial cues than previously known.
Specifically, we found that 90.1\% of MNLI matched instances contain \emph{contextless superficial cues} in the hypothesis, while 71.9\% SNLI contains \emph{contextless superficial cues}.
Additionally, we found that COPA contains not only contextless superficial cues \cite{kavumba-etal-2019-choosing}, but 78.0\% of the instances also contain \emph{context superficial cues}.
Following these results, we split the testing sets into a subset consisting of superficial cues and another control subset without superficial cues.
The subsets allow us to diagnose the models at a global level.\footnote{Further details can be found in our paper \cite{kavumba-etal-2022-prompt}}

In the next section, we examine whether few-shot prompt-based models also rely on superficial cues to achieve remarkable performance on MNLI, SNLI, COPA, and the HANS dataset \cite{mccoy2019right}.


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%%%% fig. 1
\begin{figure}[tb]
\begin{center}
\includegraphics{29-3ia22f1.pdf}
\end{center}
%%%%\label{fig:mnli-snli-hans-results}
%%%%\label{fig:mnli-snli-contextless-results}
\hangcaption{Accuracy (Acc) on subset with superficial cues (w/ cues) and without superficial cues (w/o cues). (a) Acc on the HANS dataset on lexical overlap (lexical), subsequence (subs), and constituent (consti) context superficial cues. (b) Acc on instances with and without contextless superficial cues of matched (MNLI) and mismatched (MNLI-mm); and results on SNLI.}
\label{fig01}
\end{figure}
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\section{Debugging Prompt-based Models}
\label{sec:experiments}

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\subsection{Context Superficial Cues}
\label{sec:results-context-supericial-cues}

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\paragraph{Natural Language Inference (NLI)}
%%%%Figure~\ref{fig:mnli-snli-hans-results} shows the results on the HANS dataset of the prompt-based model trained 
    Figure~\ref{fig01}a shows the results on the HANS dataset of the prompt-based model trained 
on MNLI (left) and SNLI (right), respectively.
The results show that prompt-based RoBERTa trained on MNLI performs considerably well on instances with superficial cues, an overall average of 98.7\%.
However, the model only achieves an overall average accuracy of 7.4\% on instances without context superficial cues, failing to reach random accuracy of 50\%.
This indicates that the prompt-based models trained on MNLI exploit superficial cues.

%%%%Similarly, figure~\ref{fig:mnli-snli-hans-results} (right) shows that while RoBERTa performs considerably well 
    Similarly, Figure~\ref{fig01}a (right) shows that while RoBERTa performs considerably well 
on instances with superficial cues (overall average 91.3\%), it fails to achieve the same performance on instances without superficial cues (overall average of 31.7\%).


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\paragraph{COPA}
Table~\ref{tab:copa-context-superficial-cues} shows the results of the prompt-based RoBERTa trained on COPA and evaluated on the two subsets of COPA: with and without superficial cues. The results show RoBERTa performs well on instances with superficial cues but barely exceeds random accuracy (50\%) on instances without superficial cues.

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%%%% table 1
\begin{table}[tb]
\begin{subtable}[h]{0.45\textwidth}
\begin{center}
\begin{tabular}{rccc}
\toprule
\multicolumn{1}{c}{k} & \easy                  & \hard                 & Overall       \\ \midrule
8           & $83.0 \pm 0.5$ & $55.7 \pm 1.4$ & $78.1 \pm 0.3$ \\
16          & $81.6 \pm 1.9$ & $57.9 \pm 1.0$ & $77.3 \pm 1.4$ \\
32          & $82.4 \pm 2.0$ & $53.5 \pm 0.5$ & $77.1 \pm 1.5$ \\
64          & $84.4 \pm 1.4$ & $53.8 \pm 4.1$ & $78.9 \pm 1.2$ \\
96          & $87.0 \pm 1.7$ & $57.9 \pm 4.1$ & $81.7 \pm 0.8$ \\
100         & $87.9 \pm 1.7$ & $54.2 \pm 1.9$ & $81.7 \pm 1.2$ \\ \bottomrule
\end{tabular}
\end{center}
\caption{Context Superficial Cues}
\label{tab:copa-context-superficial-cues}
\end{subtable}
\begin{subtable}[h]{0.45\textwidth}
\begin{center}
\begin{tabular}{rccc}
\toprule
\multicolumn{1}{c}{k} & \easy                & \hard                 & Overall               \\ \midrule
8           & $79.1 \pm 0.9$ & $77.4 \pm 0.0$   & $78.1 \pm 0.3$ \\
16          & $77.0 \pm 1.3$   & $77.4 \pm 1.6$ & $77.3 \pm 1.4$ \\
32          & $77.7 \pm 3.1$ & $76.8 \pm 0.9$ & $77.1 \pm 1.5$ \\
64
& $80.2 \pm 0.5$ & $78.1 \pm 1.6$ & $78.9 \pm 1.2$ \\
96          & $83.7 \pm 1.1$ & $80.4 \pm 0.7$ & $81.7 \pm 0.8$ \\
100         & $84.6 \pm 1.1$ & $80.0 \pm 1.6$   & $81.7 \pm 1.2$ \\ \bottomrule
\end{tabular}
\end{center}
\caption{Contextless Superficial Cues}
\label{tab:copa-contextless-superficial-cues}
\end{subtable}
\addtocounter{table}{-1}
\hangcaption{ (a) Average accuracy of k-shot prompt-based RoBERTa models on COPA instances with context superficial cues between the context and the answer choices; and instances without exploitable context superficial cues. Column ``k'' shows the number of examples used for finetuning. (b) Average accuracy on COPA instances with contextless superficial cues.}
\label{tab:copa-results}
\end{table}
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\subsection{Contextless Superficial Cues}

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\paragraph{NLI}
%%%%Figure~\ref{fig:mnli-snli-contextless-results} shows the results of prompt-based RoBERTa train 
    Figure~\ref{fig01}b shows the results of prompt-based RoBERTa trained 
on MNLI and SNLI and evaluated on the corresponding test set.
The results show that the prompt-based model trained on MNLI performs considerably better on instances with superficial cues on both matched (69.5\%) and mismatched (72.0\%) instances.
On instances without superficial cues, we observe a huge drop in performance on both matched and mismatched instances.
The large difference in performance indicates that the models exploit contextless superficial cues.
For the model trained on SNLI and evaluated on SNLI subsets, we observe a huge gap between performance on instances with and without superficial cues (82.2\% vs 67.1\%). 


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\paragraph{COPA}
Table~\ref{tab:copa-contextless-superficial-cues} shows of the prompt-based RoBERTa trained on COPA and evaluated on the COPA subsets with and without superficial cues.
The results show that the prompt-based model does not exploit superficial cues at a small enough training set (less or equal to 32 instances).
However, increasing the size further increases the gap in performance between instances with and without contextless superficial cues.
It is encouraging to note that the model does not exploit contextless superficial cues at sizes commonly used in few-shot settings.



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\section{Future Work}

The results show that prompt-based models, despite being trained on a few-examples also exploit superficial cues.
One interesting question that is yet to be answered is: Can prompt tuning with task demonstration that do not have superficial cues lead to better generalization?


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\bibliographystyle{jnlpbbl_1.6}
\bibliography{22refs}


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\begin{biography}

\bioauthor[:]{Pride Kavumba}{%
Currently a Ph.D. student at Tohoku University.
His interest includes Robustness \& Interpretability, Causality, Commonsense Reasoning,  Language Modeling, and NLP applications.
}

\bioauthor[:]{Ryo Takahashi}{%
PhD in Information Science, Tohoku University, 2021.
He is currently working at LegalForce, Inc.
His interest includes knowledge graph embeddings and MLOps.
}
\bioauthor[:]{Yusuke Oda}{%
Master of Engineering (2015, NAIST).
He is currently working at LegalForce, Inc. to lead a dedicated R\&D section for AI applications.
He has worked formerly at Google, Inc. and NICT for developing machine translation and peripheral technology around that area.
His interest includes machine translation, parsing, and NLP applications for software engineering.
}
\end{biography}



\end{document}



