Large Language Models Can Be Easily Distracted by Irrelevant Context · HackerPapers discussions

Large Language Models Can Be Easily Distracted by Irrelevant Context — mentioned by root-parent

Wed, 12 Aug 2026 12:52:32 GMT

>> t the more verbose you are with what you ask of it, the better the results will be. Trivially falsifiable: "Context Length Alone Hurts LLM Performance Despite Perfect Retrieval" https://aclanthology.org/2025.findings-emnlp.1264/ "Large Language Models Can Be Easily Distracted by Irrelevant Context" https://arxiv.org/abs/2302.00093

Paper and discussion history. A mention is not an endorsement.

Large Language Models Can Be Easily Distracted by Irrelevant Context — mentioned by estreeper

Mon, 06 Nov 2023 22:37:59 GMT

For embeddings specifically, there are multiple open source models that outperform OpenAI’s best model (text-embedding-ada-002) that you can see on the MTEB Leaderboard [1] > embedding-based approach will be cheaper and faster, but worse result than full text I’m not sure results would be worse, I think it depends on the extent to which the models are able to ignore irrelevant context, which is a problem [2]. Using retrieval can come closer to providing only relevant context. 1. https://huggingface.co/spaces/mteb/leaderboard 2. https://arxiv.org/abs/2302.00093

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Large Language Models Can Be Easily Distracted by Irrelevant Context — HN submission

Fri, 03 Feb 2023 18:21:20 GMT

Large Language Models Can Be Easily Distracted by Irrelevant Context

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.