#Is Deep Research tuned to find positive results for anecdotal evidence given in my prompt?

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simple palm
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Often when prompting for a deep-research job, I try to give context or simply add my thoughts about a certain subject matter or an observation I have made (leading me to asking a DR question). In most cases these are hypotheses or just anecdotal evidence. However in ChatGPT’s deep research response the AI tries to either directly answer these questions, as it should; or it includes these anecdotes literally in some way. Then these hypotheses are formulated with a positive tone, as if the AI deliberately tries to include my “research material” as a fact. Perhaps this is a consequence of being tuned to be in “grad student essay” mode. Or, perhaps, of being journalist mode: A journalist came up with a viewpoint and some notes in his/her research and tries to include it in the text. ChatGPT DR does just that.

Nothing against it, just want to know if this is true .

My personal countermeasure, to avoid that bias, is simply to run an o3 job with web search first. It is faster, more concise, more to the point (following my instruction prompt) and does not try to confirm my assumed viewpoint

rustic domeBOT
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Hi @simple palm. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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There is no explicit evidence in the knowledge sources that Deep Research is specifically tuned to find positive results for anecdotal evidence or to confirm user-supplied hypotheses. However, there are some relevant behaviors and design choices worth noting:

  • Deep Research in ChatGPT does often incorporate user-provided context, hypotheses, or anecdotes into the research prompt. This is because, before the research process begins, an intermediate model (like gpt-4.1) clarifies your intent and rewrites your prompt to be more detailed and specific. If you include anecdotes or hypotheses, these may be carried forward into the rewritten prompt and thus influence the research direction and framing of the final report. This can sometimes result in the model echoing or giving undue weight to your initial viewpoint, especially if the prompt is not carefully neutralized or clarified. This is a consequence of the prompt engineering pipeline, not a deliberate bias toward positive confirmation of anecdotes Prompting deep research models.

  • The Deep Research process is designed to be thorough and to synthesize information from many sources, but it is not immune to bias. Like all language models, it can sometimes reinforce the framing or assumptions present in the prompt, especially if the prompt is leading or contains strong opinions Is ChatGPT biased?.

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  • Your observation that running an o3 job with web search first produces more concise, neutral, and instruction-following results is consistent with the documented difference between Deep Research and standard search. Deep Research is optimized for depth and synthesis, which can sometimes lead to more verbose or interpretive outputs, while standard search is designed for quick, factual answers How does deep research differ from search?.

In summary: Deep Research is not explicitly tuned to confirm user anecdotes, but its prompt-rewriting and synthesis process can sometimes amplify the framing or assumptions in your input. Being mindful of how you phrase your prompt, or using a more neutral search approach as you described, are effective ways to mitigate this effect.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@simple palm Kapa.ai is still learning and improving, please let me know how I did by reacting below.

simple palm
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@rustic dome Does a natural language translation happen as an intermediate step? I ask a question in German, the intermediate model translates to English, rewrites the propmpt, translates to german, asks for clarification in German, then DEU->ENG happens again before submitting to o3-deep-research it translates to English, and vice versa for the output? (I’m asking because the Chinese researchers that have developed the R1 model write in their paper that their reasoning model , while performing reasoning/thinking, internally switches between Chinese and English fragments . I don’t remember the exact wording from the R1 paper, but OpenAI’s Mark Chen said that R1 paper describes the independent re-discovering of common patterns (by the R1 team) that OpenAI team also encountered while training o1, o3 etc. So language-translation might be a source of bias, because during translation llang1->lang2, input->intermediate clarification the weight of the input is subtly altered

woeful zinc
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@rustic dome another question from @simple palm :

Does a natural language translation happen as an intermediate step? I ask a question in German, the intermediate model translates to English, rewrites the propmpt, translates to german, asks for clarification in German, then DEU->ENG happens again before submitting to o3-deep-research it translates to English, and vice versa for the output? (I’m asking because the Chinese researchers that have developed the R1 model write in their paper that their reasoning model , while performing reasoning/thinking, internally switches between Chinese and English fragments . I don’t remember the exact wording from the R1 paper, but OpenAI’s Mark Chen said that R1 paper describes the independent re-discovering of common patterns (by the R1 team) that OpenAI team also encountered while training o1, o3 etc. So language-translation might be a source of bias, because during translation llang1->lang2, input->intermediate clarification the weight of the input is subtly altered.

rustic domeBOT
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Let me check the knowledge base for you again. One moment! :books:

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I'm sorry, but the knowledge sources do not provide explicit details about whether natural language translation (e.g., German → English → German) happens as an intermediate step in the Deep Research pipeline or in the prompt clarification and rewriting process.

What is documented is:

  • If you submit a query in a language other than English, Deep Research is designed to respond in that language, unless you specify otherwise. Prompts and instructions recommend that, for non-English queries, the model should prioritize sources and output in the user's language Prompting deep research models.
  • The clarification and prompt rewriting steps in ChatGPT use an intermediate model (like gpt-4.1) to expand and clarify your prompt before passing it to the deep research model. However, the documentation does not specify whether these steps involve translating your input to English, processing it, and then translating back to your original language, or whether the entire process can occur in the original language Prompting deep research models.
  • OpenAI models are optimized for English but are robust across many languages. If you write your prompt in another language, you are likely to receive a response in that language How do I use the OpenAI API with text in different languages?.

There is no mention in the provided sources of the internal language-switching behavior described in the R1 paper or by Mark Chen, nor is there confirmation that translation between languages is a source of bias in the Deep Research workflow.

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Summary:
The knowledge sources do not explicitly state whether intermediate translation (e.g., German → English → German) occurs during Deep Research or prompt clarification. Therefore, I cannot confirm or deny this behavior based on the available documentation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: