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Articles tagged with #llm
Self-correction/critiquing is a methodology proposed to improve the accuracy and appropriateness of the generated content by Large Language Models (LLMs). It involves an LLM reviewing its own responses, identifying problems or errors, and revising it...
Language models often struggle to use information in the middle of long input contexts, and that performance decreases as the input context grows longer. In a recent paper by NF Liu et. al, they discovered to get the best results with RAG (Retrieva...
The Retrieval-Augmented Generation (RAG) framework enhances the quality of the Large Language Model (LLM) generated responses by grounding them on external sources, reducing hallucinations or fabricated information. Significantly, RAG allows for the ...
Rebuff.ai:Rebuff offers 4 layers of defense:- Heuristics: Filter out potentially malicious input before it reaches the LLM.- LLM-based detection: Use a dedicated LLM to analyze incoming prompts - and identify potential attacks.- VectorDB: Store embed...
Prompt injections 馃拤 can manipulate language models like ChatGPT when connected to malicious information sources controlled by attackers. This vulnerability is similar to running untrusted code on a computer, but with natural language instructions fo...
Information retrieval aims to locate text-based information relevant to a query within a substantial pool of potential sources. In its early stages, sparse retrieval primarily emphasized term matching using sparse representations and inverted indexes...
