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    <titleInfo>
        <title>Efficiently Computing Susceptibility to Context in Language Models</title>
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    <name type="personal">
        <namePart type="given">Tianyu</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Kevin</namePart>
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    <name type="personal">
        <namePart type="given">Mrinmaya</namePart>
        <namePart type="family">Sachan</namePart>
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    <name type="personal">
        <namePart type="given">Ryan</namePart>
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            <namePart type="given">Mohit</namePart>
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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
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    <abstract>One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the subtle changes to that context.To quantify this, Du et al. (2024) gives an information-theoretic metric to measure such sensitivity. Their metric, susceptibility, is defined as the degree to which contexts can influence a model’s response to a query at a distributional level.However, exactly computing susceptibility is difficult and, thus, Du et al. (2024) falls back on a Monte Carlo approximation.Due to the large number of samples required, the Monte Carlo approximation is inefficient in practice. As a faster alternative, we propose Fisher susceptibility, an efficient method to estimate the susceptibility based on Fisher information.Empirically, we validate that Fisher susceptibility is comparable to Monte Carlo estimated susceptibility across a diverse set of query domains despite its being 70\times faster.Exploiting the improved efficiency, we apply Fisher susceptibility to analyze factors affecting the susceptibility of language models.We observe that larger models are as susceptible as smaller ones.</abstract>
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        <url>https://aclanthology.org/2024.findings-emnlp.386</url>
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%0 Conference Proceedings
%T Efficiently Computing Susceptibility to Context in Language Models
%A Liu, Tianyu
%A Du, Kevin
%A Sachan, Mrinmaya
%A Cotterell, Ryan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-efficiently
%X One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the subtle changes to that context.To quantify this, Du et al. (2024) gives an information-theoretic metric to measure such sensitivity. Their metric, susceptibility, is defined as the degree to which contexts can influence a model’s response to a query at a distributional level.However, exactly computing susceptibility is difficult and, thus, Du et al. (2024) falls back on a Monte Carlo approximation.Due to the large number of samples required, the Monte Carlo approximation is inefficient in practice. As a faster alternative, we propose Fisher susceptibility, an efficient method to estimate the susceptibility based on Fisher information.Empirically, we validate that Fisher susceptibility is comparable to Monte Carlo estimated susceptibility across a diverse set of query domains despite its being 70\times faster.Exploiting the improved efficiency, we apply Fisher susceptibility to analyze factors affecting the susceptibility of language models.We observe that larger models are as susceptible as smaller ones.
%U https://aclanthology.org/2024.findings-emnlp.386
%P 6615-6626

Markdown (Informal)

[Efficiently Computing Susceptibility to Context in Language Models](https://aclanthology.org/2024.findings-emnlp.386) (Liu et al., Findings 2024)

• Efficiently Computing Susceptibility to Context in Language Models (Liu et al., Findings 2024)

ACL

• Tianyu Liu, Kevin Du, Mrinmaya Sachan, and Ryan Cotterell. 2024. Efficiently Computing Susceptibility to Context in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6615–6626, Miami, Florida, USA. Association for Computational Linguistics.