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{
  "$type": "app.bsky.feed.post",
  "createdAt": "2025-12-22T04:06:43.179Z",
  "embed": {
    "$type": "app.bsky.embed.external",
    "external": {
      "description": "Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent reasoning when expose...",
      "thumb": {
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        "ref": {
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        "mimeType": "image/jpeg",
        "size": 147000
      },
      "title": "Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals",
      "uri": "https://arxiv.org/abs/2502.16101"
    }
  },
  "facets": [
    {
      "features": [
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          "uri": "https://arxiv.org/abs/2502.16101"
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  "langs": [
    "en"
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  "reply": {
    "parent": {
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      "uri": "at://did:plc:ttdrpj45ibqunmfhdsb4zdwq/app.bsky.feed.post/3make4s3vk22a"
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    "root": {
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  },
  "text": "RAG like this feels like an anti pattern tbh especially when results shown to the model contradict each other  arxiv.org/abs/2502.16101"
}