Asked Claude:
You’ve raised an interesting question about LLMs and their performance on regulatory and legislative topics. There are indeed some reasons why these domains present particular challenges:
Complexity and specificity - Legal and regulatory texts are often highly specific, with precise wording that carries significant meaning. Small differences in interpretation can lead to completely different understandings of requirements or permissions.
Domain-specific language - Legal texts use specialized vocabulary and constructions that can be difficult to parse correctly without domain expertise.
Contextual understanding - Laws and regulations often require understanding a web of interconnected documents, precedents, and interpretations to be properly understood.
Frequent changes - Regulations and laws change regularly through amendments, court decisions, and new legislation, making it difficult for models with fixed training cutoffs to stay current.
Less representation in training data - Compared to general knowledge topics, detailed regulatory texts may be less represented in training datasets.
There is empirical evidence suggesting this is a real phenomenon rather than just your impression. Research has shown that LLMs tend to perform worse on tasks requiring deep domain knowledge in specialized fields like law compared to more general reasoning tasks.
The challenge is particularly pronounced when asking about specific provisions or requirements rather than general principles. Models might confidently generate plausible-sounding but incorrect interpretations of regulatory requirements, which is especially concerning in a domain where accuracy is crucial
Contextual understanding of interconnected documents presents several fundamental challenges for LLMs:
Memory and attention limitations - LLMs have a fixed context window, making it difficult to simultaneously process multiple lengthy documents with all their cross-references. Even with expanded context windows, managing attention across extensive interconnected content remains challenging.
Reference resolution complexity - Legal and regulatory documents often use complex reference systems (“as stated in paragraph 4 of section 12(b) as amended by…”). These references require precise tracking across multiple documents to resolve correctly.
Temporal relationships - Understanding which version of a document applies at what time period is crucial. Laws are amended, regulations updated, and precedents established or overturned in a complex temporal sequence.
Hierarchical structure - Legal frameworks have implicit hierarchies (constitutions > statutes > regulations) that determine how conflicts are resolved. Models struggle to internalize these precedence rules.
Domain-specific inference - Legal reasoning requires specific types of inference that differ from everyday reasoning. For example, understanding when a general rule is modified by a specific exception requires specialized inference patterns.
Definitional propagation - A term defined in one document may be used in multiple other documents, with the definition propagating throughout the system. Missing this initial definition can lead to cascading misunderstandings.
Contextual terminology - The same term may have different meanings in different legal contexts or jurisdictions, requiring the model to track which meaning applies where.
Implicit knowledge - Legal documents often assume knowledge of standard practices, conventions, and interpretations that aren’t explicitly stated but are essential for proper understanding.
This complexity is particularly challenging because errors compound - misunderstanding one document or relationship often leads to incorrect interpretations of other connected documents, creating a cascade of reasoning failures.
Strategies for managing this:
To get reliable legislative information from LLMs, I recommend these approaches:
Use explicit framing - Start with “According to [specific legislation/regulation]…” to constrain the model’s response to a particular source.
Request step-by-step analysis - Ask the model to first identify the relevant laws, then explain its understanding of them, and finally apply them to your specific situation.
Ask for direct citations - Request specific section numbers, paragraph references, and exact quotes from the legislation.
Verify with multiple queries - Cross-check by asking related questions that would reveal inconsistencies in the model’s understanding.
Request confidence levels - Ask the model to indicate its confidence in different parts of its response.
Use triangulation - Ask the same question in different ways to see if you get consistent answers.
Break complex questions into smaller components - This helps the model focus on specific aspects rather than synthesizing complex legal information.
Request sources for verification - Ask for official government websites or databases where you can verify the information.
Challenge the model’s reasoning - Ask “What might be incorrect about your analysis?” to encourage critical reflection. >
Combine LLM assistance with traditional research - Use the LLM to help you navigate to the right sources, but verify crucial details through official legal resources.
Remember that no matter how sophisticated the approach, LLM outputs should always be treated as starting points for legal research, not as definitive legal advice. For critical legal matters, consulting with a qualified legal professional remains essential.