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Citation tool offers a new approach to trustworthy AI-generated content
- MIT CSAIL researchers created ContextCite to improve trust in AI-generated content that uses external context for answering queries.
- ContextCite helps identify the sources of external context used by a model to generate any particular statement that can enhance users' trust to verify the statement.
- Researchers perform context ablations, where specific external data pieces are removed from the dataset to test the model's response, to make ContextCite possible.
- ContextCite can help improve the quality of AI's responses by identifying and pruning irrelevant context while producing more accurate responses.
- The tool can detect poisoning attacks where malicious actors insert statements that trick AI assistants into leaking personalized information.
- ContextCite reveals the exact source material being used by a model to form its response to guideline the model's approach effectively
- ContextCite can be used in industries that require high levels of accuracy, like healthcare, law, and education.
- The scientists are aware of issues of complexity of language, but they recognize the importance of further refinement to address these complexities.
- ContextCite strives to build itself as a fundamental component to achieve reliable and attributable insights generated by AI
- The researchers will present their findings at the Conference on Neural Information Processing Systems this week.
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