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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2412.15260/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2412.15260/feed.xml" rel="self" type="application/rss+xml" /><item><title>Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice — mentioned by HannesWes</title><link>https://news.ycombinator.com/item?id=43047760</link><guid isPermaLink="false">urn:hackerpapers:mention:2412.15260:43047760</guid><pubDate>Fri, 14 Feb 2025 12:39:34 GMT</pubDate><description>&lt;p&gt;This looks very interesting. I conducted some explorations of whether LLMs can be used to extract information from hand-written forms [0][1]. Such a system could allow users to snap pictures of forms and other legal documents, automatically extract structured information, and use this information to e.g. automatically fill out new forms or determine whether the user has the right to a government benefit. The initial results were quite promising, as GPT-4o could reliably identify the correct place in the form for the information, and moderately reliably extract the values, even if the image…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2412.15260/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
