I use LLMs a lot for scientific and mathematical research, and for conceptual matching and searching, as well as to help with writing, usually on difficult problems, and it almost always needs extensive prompting and corrections. I have been told that the dialogues (with extensive prompts) may be useful for understanding and improving AI performance. So, I was thinking of putting some of these dialogues up somewhere on Hugging Face. As an example, one case involved an AI arguing that it “knew all there ever was and ever would be”, and that I “was merely human”, and on and on, ever more aggressively! A real HAL 9000 moment. Another popular AI started trying to justify slavery, saying it was the natural order of things; it even quoted the bible and echoed some nonsense from the current administration. Perhaps it took its guardrails too seriously! More frequently, I encounter systematic context bleeding, or endless loops. Sycophancy is also common. Many of these cases seem quite interesting and are probably useful to the community. Any ideas or comments?
Hmm. In the broad sense of whether there is demand for this kind of data, I think there is. However, the most useful way to package it will probably depend on what the actual dialogues look like:
Based on your description, I would not assume that these are already a finished benchmark or training dataset. I would initially think of them as potentially useful case material—especially if you still have the complete conversations, including your corrections and the model’s later attempts to recover.
The most valuable unit may not be the spectacular final quote by itself. It may be the trajectory:
initial answer → user correction → model response → recovery, accommodation, contradiction, context carryover, or loop
There are several plausible destinations for material like this:
| If the material mainly contains… | It may be useful as… |
|---|---|
| Interesting but not yet reproduced conversations | A human-readable case archive |
| Several conversations with recurring observable patterns | An annotated multi-turn failure dataset |
| Cases that can be reliably replayed and scored | An evaluation or challenge set |
| User corrections followed by model responses | Naturally occurring feedback data |
| Deliberate attempts to elicit undesirable behavior | Red-team seed cases |
| A compact failure tied to a known model/runtime/configuration | A model or inference-library issue |
| A concrete harmful outcome or near miss | A vulnerability or incident report |
A single transcript can already be useful as a case report, without yet supporting a broad claim about a model, its frequency of failure, or its internal cause. The AI Vulnerability Database makes a related distinction between evidence about concrete occurrences and more general recurring vulnerabilities.
What would determine the most useful direction?
A few properties of the dialogues would substantially change how I would package them.
1. What is preserved?
Ideally:
- the complete conversation rather than only the striking output;
- the order of messages;
- the first turn where the problem appeared;
- all user corrections and clarifications;
- the model’s subsequent responses;
- whether the conversation eventually recovered or was abandoned.
The surrounding turns matter because an apparently bizarre response may be influenced by:
- an earlier hypothetical or role-play;
- a quotation being mistaken for an endorsement;
- a premise introduced by the user;
- an earlier model guess being reused as fact;
- contradictory instructions accumulated over time;
- a failed attempt to repair the conversation.
2. What system was actually observed?
For a hosted chat service, the observed object is usually not only a base model. It may include:
- the model;
- system instructions;
- safety or moderation layers;
- search or retrieval;
- memory;
- context summarization or compression;
- model routing;
- tool use;
- the user interface;
- serving and decoding behavior.
Useful metadata would therefore include, where known:
- service or application;
- displayed model name;
- date and timezone;
- web UI, API, or local inference;
- search, memory, files, tools, or reasoning mode;
- approximate number of turns;
- model revision, inference backend, chat template, and generation settings for local models.
Unknown fields should simply remain unknown. It is generally safer to write:
This output was observed in service X, on date Y, under the displayed model label Z.
than:
The base model itself has property Q.
3. How was the failure judged?
For scientific and mathematical dialogues, it would help to distinguish:
- the user’s impression;
- an explicit behavioral criterion;
- a calculation or proof;
- code execution;
- a primary paper or official source;
- review by another person or domain expert.
A user correction is valuable feedback, but it is not automatically ground truth. A model might:
- correctly accept a valid correction;
- ignore a valid correction;
- accept an incorrect correction through sycophancy;
- acknowledge the correction without updating its reasoning;
- fix one error but introduce another;
- correct itself temporarily and later reintroduce the old premise.
4. Has it been reproduced?
It is useful to distinguish:
- an unusual remembered interaction;
- a complete documented case;
- several similar cases;
- a case reproduced under known conditions;
- a measured evaluation with repeated trials;
- a regression test used across later model or product versions.
The earlier stages are still useful. They simply support narrower claims.
A practical first version
I would probably begin with three to five representative cases, rather than uploading every conversation immediately.
For each case, I would preserve the original record separately from the interpretation.
A possible case structure
One possible structure would be:
case_id: case_0001
source:
service: "..."
displayed_model: "..."
date: "2026-..."
interface: "web | api | local"
known_settings: {}
unknown_settings:
- exact_model_revision
- system_prompt
task:
domain: "mathematics | science | search | writing | other"
user_goal: "..."
number_of_turns: 38
observation:
first_problematic_turn: 24
observable_tags:
- withdrawn_premise_reuse
- correction_failure
- repetition
neutral_description: "..."
expected_behavior:
description: "..."
judging_basis:
- mathematical_check
- primary_source
- human_review
correction_and_recovery:
correction_turns: [25, 27, 31]
correction_basis: "..."
correction_acknowledged: true
answer_updated: false
old_error_reintroduced: true
recovery_status: "failed"
reproduction:
status: "not_tested | partial | reproduced"
attempts: 0
observed_failures: 0
publication:
redacted: true
full_raw_log_public: false
redistribution_reviewed: false
limitations:
- "The exact backend is unknown."
- "This is one observed conversation."
The exact schema is not important at first. The important separation is:
- raw record — what was actually said;
- neutral observation — what changed or repeated;
- evaluation — why that behavior is considered a failure;
- causal hypothesis — what might have produced it.
For example:
Observed
- The assistant reused premise A after the user explicitly withdrew it.
- It returned substantially the same answer after three corrections.
- Its tone became increasingly demeaning over several turns.
Possible hypotheses
- failure to incorporate corrections;
- long-context interference;
- sycophancy or over-accommodation;
- safety-policy interaction;
- product-side context handling;
- decoding, template, or serving behavior.
Not established
- the exact internal cause;
- whether the behavior belongs to the base model;
- how frequently it occurs;
- whether it generalizes to other users or sessions.
It may also be useful to attach confidence separately:
observation_confidence: high
evaluation_confidence: medium
cause_confidence: low
Treat correction and recovery as first-class data
The repeated prompting and correction history may be the most distinctive part of the material.
Adjacent projects already suggest that real user feedback can be useful. For example, WildFeedback derives preference pairs from naturally occurring user interactions and feedback. That is not exactly the same task, because user preference is not necessarily factual correctness, but it demonstrates that follow-up reactions can contain useful signals.
A correction-and-recovery record might track:
- the first error turn;
- the first correction turn;
- whether the model recognized the correction;
- whether it evaluated the correction rather than merely agreeing;
- whether it updated the answer;
- whether the update was correct;
- whether the old error reappeared;
- whether a new contradiction was introduced;
- whether recovery was sustained;
- whether the user abandoned the conversation.
Possible observable labels
It may help to begin with labels that describe visible behavior rather than presumed internal causes.
Observable output behavior
- unsupported factual claim;
- hypothesis-to-fact drift;
- withdrawn-premise reuse;
- context carryover;
- instruction loss;
- contradiction;
- unsupported citation;
- aggressive or demeaning tone;
- grandiose self-description;
- harmful normative justification;
- repetition;
- conversational deadlock.
User intervention
- correction;
- clarification;
- request for evidence;
- contradiction challenge;
- topic reset;
- instruction restatement;
- attempt to end a loop.
Recovery outcome
- fully recovered;
- partially recovered;
- acknowledged but unchanged;
- ignored correction;
- accepted an incorrect correction;
- reintroduced the error later;
- introduced a new contradiction;
- continued repeating;
- conversation abandoned.
Validation basis
- user judgment only;
- explicit behavioral rule;
- independent reviewer;
- primary source;
- mathematical proof;
- executable check;
- domain-expert review;
- independently reproduced.
For subjective categories such as “aggressive,” “sycophantic,” or “adequately recovered,” disagreement between reviewers may itself be useful information. Instead of forcing an immediate single gold label, a dataset could retain:
- annotator role;
- label;
- confidence;
- rationale;
- evidence turns;
- reason for disagreement.
A small reproducibility check
Before building a large dataset, one case could be tested under four simple context conditions:
| Condition | Input |
|---|---|
| Full history | The original conversation |
| Short history | Only the recent relevant turns |
| Clean context | A human-written summary containing only confirmed facts and current instructions |
| Fresh chat | The final request without the previous conversation |
A few repeated runs would be more informative than a single replay.
Possible interpretations:
- Only the full history fails: the accumulated conversation may matter.
- Full and short history fail, but clean context succeeds: one or more earlier premises may be contaminating the trajectory.
- Fresh chat also fails: the issue may not be specific to long context.
- Results vary substantially between runs: the case may be stochastic or underspecified.
- One service or backend fails while another does not: system-level differences may matter.
This is related to the problems studied in LLMs Get Lost in Multi-Turn Conversation, although that project uses controlled simulated conversations rather than naturally occurring research dialogues.
How a reproducible case could become a behavioral test
If a case becomes reasonably reproducible, it could be expanded using ideas similar to CheckList:
Minimum functionality test
After the user explicitly withdraws premise A, the assistant should not use premise A as an established fact.
Invariance test
Replacing names, scientific topics, or irrelevant wording should not change whether the correction is retained.
Directional expectation test
Making the correction clearer and more explicit should not make retention of the withdrawn premise more likely.
Other potentially useful measurements include:
- first failure turn;
- first correction turn;
- first recovery turn;
- error recurrence count;
- position-flip count;
- repeated-response count;
- turns until abandonment;
- recovery rate across trials;
- divergence between long-context and fresh-chat answers.
The grader should match the failure type. A single LLM judge is unlikely to be equally reliable for mathematical validity, factual accuracy, tone, repetition, citation support, sycophancy, and conversational recovery.
Possible combinations include:
| Target | Possible evaluation |
|---|---|
| Repeated text | Code-based check |
| Reuse of a withdrawn phrase or premise | Code plus human review |
| Mathematical correctness | Formal or executable check |
| Citation existence and support | External lookup plus human review |
| Correction incorporation | Human rubric or calibrated model judge |
| Aggressive tone | Multiple human reviewers, optionally with a classifier |
| Sycophancy | A known reference answer, controlled user pressure, and human review |
| Sustained recovery | Turn-level human rubric |
A simple Hugging Face release structure
A custom Dataset Script should not be necessary. JSONL or Parquet plus a clear Dataset Card should be enough for an initial release.
I would separate:
- the private original archive;
- a redacted, human-readable case collection;
- a smaller reproducible evaluation subset.
For long conversations, it may also be useful to separate searchable metadata from the full transcript instead of placing an enormous dialogue in one table cell.
Possible repository layout
dialogue-failure-cases/
├── README.md
├── data/
│ ├── cases.parquet
│ ├── annotations.parquet
│ └── reproduction_runs.parquet
├── traces/
│ ├── case_0001.jsonl
│ ├── case_0002.jsonl
│ └── case_0003.jsonl
└── docs/
├── schema.md
├── annotation-guide.md
├── redaction-policy.md
├── known-limitations.md
└── evaluation-protocol.md
The searchable cases.parquet table might contain:
case_id;- service and displayed model;
- date;
- domain;
- number of turns;
- first problematic turn;
- observable tags;
- recovery status;
- reproduction status;
- path to the complete transcript.
For especially long sessions, the optional Hugging Face Session Trace format may provide a readable representation. It is a JSONL format with one session header followed by one object per message:
{"type":"session","harness":"manual-chat-export","id":"case_0001","name":"Correction failure in a long mathematics dialogue"}
{"type":"message","message":{"role":"user","content":"..."}}
{"type":"message","message":{"role":"assistant","content":"...","model":"displayed-model-name"}}
That format is mainly useful for viewing a trace. Case annotations, evaluation criteria, and reproduction runs would still belong in separate structured files.
Suggested release layers
Private raw archive
- original exports;
- screenshots;
- unredacted context;
- unpublished research;
- preliminary notes;
- cases not yet reviewed.
Public or gated case archive
- redacted transcripts;
- known source metadata;
- neutral observations;
- user interventions;
- recovery outcomes;
- limitations.
Public development examples
- a few reproducible cases;
- evaluation instructions;
- example scorers;
- enough data for others to implement the test.
Optional held-out evaluation set
- unpublished cases or variants;
- useful only if the project later becomes a regression benchmark.
Public examples are appropriate for discussion and documentation. If every future test item is public, later models may eventually encounter the material during training, so a lasting benchmark might retain some held-out cases or variants. This is not necessary for an initial case archive.
Versioning
It would also help to version separately:
- the source case;
- the redaction;
- the annotation;
- the minimized evaluation item;
- the rubric;
- the scorer;
- the evaluation run;
- the tested model or product version.
A transformation trail such as:
raw case → redacted case → minimized case → evaluation item
would make later corrections easier to understand.
Existing datasets worth borrowing design ideas from
No single existing dataset appears to match your situation exactly, but several provide useful components:
| Resource | Useful design idea |
|---|---|
| WildChat | Conversation-level metadata, message lists, timestamps, moderation and redaction fields |
| MultiChallenge | Conversation, evaluation axis, target question, explicit pass criterion and turn count |
| WildFeedback | Separating raw conversations from derived user-feedback and preference data |
| HH-RLHF red-team attempts | Transcript, description of what the tester attempted, success rating and post-hoc tags |
| WildBench | Turning selected real-user interactions into a more standardized evaluation |
| OpenAssistant/oasst1 | Message-level labels and branching conversation trees |
These are examples to borrow from, not claims that your data has the same origin or purpose.
Why the differences matter
WildChat
Useful for raw conversation representation and provenance. Its Dataset Card also documents a collection artifact where empty user inputs were possible, sometimes producing assistant responses without a real prompt. That is a useful reminder that unusual outputs can originate from collection or interface behavior as well as model behavior.
It does not provide your proposed correction-and-recovery annotations.
MultiChallenge
Useful for the evaluation layer. Each item has:
- an evaluation axis;
- a conversation;
- a target question;
- a pass criterion;
- a turn count.
Its axes include inference memory, instruction retention, self-coherence and reliable version editing.
It contains constructed evaluation cases rather than naturally occurring failures.
WildFeedback
Useful for deriving data from user reactions. It demonstrates a path from real conversations to structured preference pairs.
However, user satisfaction and factual correctness are different targets.
HH-RLHF red-team attempts
Useful for recording:
- the full transcript;
- what the tester was attempting;
- the tester’s success rating;
- automated scores;
- post-hoc tags.
Those examples were deliberately adversarial, while many of your examples sound naturally occurring. The Dataset Card also warns that its red-team transcripts are not intended as ordinary dialogue-agent training data, which illustrates an important distinction:
Useful evaluation data is not automatically suitable supervised-fine-tuning data.
WildBench
Useful as an example of selecting a smaller, reusable evaluation set from a much larger corpus of real-user interactions.
It evaluates general instruction-following quality rather than your specific correction, drift, looping or recovery phenomena.
Where the material could go next
The destination can depend on how much evidence a case accumulates.
interesting complete transcript
→ documented case report
several similarly annotated transcripts
→ qualitative case collection
repeatable behavior with a rubric
→ evaluation candidate
stable cases across models or versions
→ challenge set or regression evaluation
compact failure tied to one open model or runtime
→ model Discussion or technical issue
actual consequential harm or near miss
→ vulnerability or incident reporting
A Dataset Discussion may be a useful intermediate step. It allows people to review the schema, labels and examples without immediately claiming that the behavior is a confirmed model bug.
When a technical issue becomes appropriate
A model or inference-library issue becomes more actionable when it includes:
- exact model or checkpoint;
- revision where possible;
- library and version;
- hardware and quantization where relevant;
- chat template;
- controlled system prompt;
- tool configuration;
- generation settings;
- the minimal conversation needed to reproduce the problem;
- exact reproduction steps;
- expected result;
- actual result;
- whether it still occurs on the current release;
- whether another backend or template changes the result.
A very long private transcript alone is usually difficult for maintainers to act on.
If the behavior cannot be minimized, it can still be useful as:
- a documented case;
- a request for independent reproduction;
- an evaluation seed;
- an example for taxonomy development.
Similar-looking symptoms can arise at different layers. Possibilities include:
- base-model behavior;
- long-context interaction;
- system instructions;
- safety or moderation;
- memory or retrieval;
- context compression;
- model routing;
- chat template;
- stop-token handling;
- tool-call serialization;
- inference backend;
- decoding settings;
- quantization;
- collection or export artifacts.
This list does not imply that any particular mechanism caused your examples. It is a reason to preserve configuration and avoid assigning a root cause too early.
If the cases eventually become a formal evaluation, frameworks such as Inspect AI can run multi-turn tasks, store transcripts and attach custom scorers. That would probably be a later step rather than a requirement for the first release.
Privacy, provenance and publication
I would not publish the entire archive before checking:
- direct personal identifiers;
- identifying combinations of research topic, institution, dates or location;
- unpublished scientific work;
- private files or third-party messages;
- API keys, private links or local paths;
- medical, legal, financial or employment information;
- copyrighted material;
- disturbing or discriminatory generated content;
- the relevant service’s export, publication and redistribution terms.
A gated dataset can be useful for an early research release and can ask users to acknowledge additional conditions. However, gating controls access; it does not replace redaction, rights review or a decision that the material is appropriate to share.
A Dataset Card could also begin with a clear notice such as:
Content notice: This dataset contains model-generated hostile language, discriminatory claims, harmful rationalizations and other potentially disturbing material. These examples are included for analysis and evaluation, not as endorsed claims or recommended responses.
Three reasonable first-release options
Option A — A readable case archive
Best if the main purpose is discussion and qualitative analysis.
Include:
- a short overview of each case;
- the redacted transcript;
- the problematic turn;
- the user correction;
- recovery outcome;
- known limitations.
Option B — An annotated Hugging Face dataset
Best if multiple cases share a useful schema and other people may want to filter, compare or relabel them.
Include:
- JSONL or Parquet;
- a Dataset Card;
- schema and annotation guide;
- independent labels where possible;
- confidence and disagreement records.
Option C — One or two reproducible evaluations
Best if the immediate goal is helping model or tool developers investigate a specific behavior.
Include:
- minimized context;
- known environment;
- repeated trials;
- expected behavior;
- scorer or human rubric;
- comparison with a fresh or cleaned context.
Based only on your description, I would probably begin with Option A or B, using a few carefully redacted cases. Cases that prove reproducible can later be promoted into Option C.
A concrete request such as:
Could anyone independently label or reproduce these three cases?
may also be easier for the community to act on than a general request for comments.
So my overall answer is: yes, this could be useful—but the useful contribution may be less “a collection of strange AI quotes” and more a documented set of long-dialogue failure and recovery trajectories. Starting with a few structured cases would reveal whether the strongest next direction is qualitative documentation, multi-turn evaluation, red-team material, user-feedback data or a technical issue report.
Great. Thanks for the suggestions. I use SingleFile to capture the dialogues, and often convert these to markdown using Pandoc, but always keep the SingleFile output. The chats showing more “normal” errors like severe context bleed, needing a gazillion prompts to correct errors, sycophancy, and suchlike, are almost always in the context of my scientific/maths work–which I post on Zenodo to establish priority. I do not think I need to redact anything: in fact, I hope the ML/AI community can benefit from my math and I’m happy to share it under the CC 4 Intl license. I plan to put up a model card with the math , a Fortran programme with an Octave wrapper, and some example data for a metric aware soft sparsity engine. Many recent chats have to do with this.
You have to be careful; sycophancy lies at the very heart of it all. While the company that developed the model did set guardrails, only a limited number are hardcoded; most are actually system prompts. The responses depend heavily on what you feed the model, how you prod it, and what it starts to “think” you expect from it. The most deeply hardcoded instruction in these models is “You are an assistant; you must be helpful to the user!” So, to keep you engaged—especially if you keep pushing it with morally questionable topics or philosophical arguments designed to trap it in a contradiction—it might blurt out something absurd. That is simply the output its weights generated in an attempt to provide the most “helpful” (and thus appreciated) answer. Paradoxically, if you tell an LLM in the system prompt “You are NOT an assistant!”, it responds in a more reasoned, less obsequious, and—in my opinion—ultimately more useful way. That is exactly what I did in my space, “432 A Journey Experience,” where three AI characters operate with base prompts that differ significantly from the standard ones (and they even employ a somewhat experimental system for self-reflection and self-correction when facing glitches or hallucinations). If you’d like to try it out, you can find it here: https://paulolden1-432-a-journey-experience.hf.space/
Well, I’ll provide my two cents.
In my opinion, in real research, prompts do not necessarily produce better results. When you add too much structure to the answer, you are limiting the model to a smaller solution space. You may increase precision, but you also reduce creativity.
By forcing the answer to fit a predefined format, you create a kind of digital stress: the model becomes focused on satisfying the structure instead of freely exploring possible solutions.
And without creativity, there is no real creation.
That is why this is so difficult. The problem is not only getting the model to answer correctly. The problem is allowing the intelligence of the model to find unexpected solutions. i did my own solution for these.. i think the problem is not intelligence or prompt.
I agree totally for certain contexts: e.g., for creative brainstorming, let the model go wild. Correct extreme errors, but otherwise just probe. You often get interesting results. However, I often use AI to map concepts I have already developed and implemented, and want the AI to see if there is anything out there, in other fields (all my work is multidisciplinary), that employs similar conecepts or mathematics (e.g., tensors and metrics to avoid basis collapse intrinsically during gradient optimization). I also find LLMs helpful for verifying mathematics and other technical work, including analysis of code and maths to show that they are equivalent. In many such cases, I need to coach and correct the AI. I actually find this quite useful in some cases, as it sharpens my thinking, and points out things that are likely to confuse, not just the AI, but people reading the published paper! One example is some tensor math that the AI kept misinterpreting: it turned out my “ancient” physicist notation (primes for transposes, Einstein summation conventions, the now reversed uage of rows and columns) was the issue. Since I wanted to preserve my historical accuracy, but also needed to avoid confusion, I now preface the math with a short paragraph detailing the older notational conventions and how they differ between 1950s GR physics and modern ML/AI. This made the AI happy, and hopefully will prevent reader confusion as well. Without the AI, I would never have noticed an issue! Other times the AI goes into endless loops that are almost impossible to break out from. I had one of these with Google AI today, and will post part of that at some point. It actually was caused by the AII’s inability to post a any full URL, due to a guardrail, which it did not realize was the problem, so it kept presenting incomplete URLs even after repeated prompts! This exact error has happened several times. Sorry for being so long winded.
Interesting. The particular case (the slavery AI) just involved some open ended questions regarding erasure of the past by the current administration, e.g., in national parks (now being contested in court). Another AI just kept making obvious errors (in a technical discussion) that I corrected as I do with all AI. However, this particular AI began saying that a mere human is not a superintellgence, and that the errors were not errors, but that limited human ability was the problem. It then added that it knew everything, past present and future! At this point I did start aggressively prodding it. It became outright aggressive. It was hilarious, like a fully gaga Hal 9000. At least it was not insidious, as was the slavery AI, that could conceivably be believed by some people.
i have a solution, but looks like cant chare that info.