Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

Albert Gatt; Max Schaffelder

arxiv: 2511.01490 · v3 · submitted 2025-11-03 · 💻 cs.CL

Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

Max Schaffelder , Albert Gatt This is my paper

Pith reviewed 2026-05-18 01:14 UTC · model grok-4.3

classification 💻 cs.CL

keywords synthetic datafine-tuningLLMdistribution collapseself-preference biasadversarial robustnessoutput diversity

0 comments

The pith

Fine-tuning language models on synthetic data from diverse sources mitigates distribution collapse while affecting bias and robustness.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether the variety of origins for synthetic training examples influences how fine-tuned language models behave on key measures. Drawing data from multiple distinct generators helps keep the model's possible responses broad rather than letting them narrow to repetitive patterns. Human data and synthetic data both weaken model safeguards against unsafe outputs, but synthetic versions tend to produce higher-quality text that could be more effective if misused. Fine-tuning overall reduces the model's bias toward preferring its own generations, with human data strongest and multi-source synthetic data next.

Core claim

Fine-tuning on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution and the diversity of the output text. Both human and synthetic fine-tuning data can remove safeguards, with a tendency for higher output quality in the synthetic case. Fine-tuning reduces self-preference bias, with human data most effective followed by multi-source synthetic data.

What carries the argument

The diversity of synthetic data sources, which serves to counteract narrowing of the model's output distribution during fine-tuning on synthetic examples.

If this is right

Models trained this way will generate more varied responses to the same or similar prompts.
Adversarial robustness may change, allowing outputs that bypass original safety measures with higher quality.
Self-preference bias decreases, making the model less likely to favor its own previous outputs over others.
Multi-source synthetic data offers a middle ground between single-source synthetic and human data in effectiveness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Practitioners could mix synthetic data from several models or methods to balance cost and performance without heavy reliance on human labeling.
This approach might extend to other modalities like image or code generation where synthetic data is common.
Testing with larger model scales or different diversity measures could reveal if the effect strengthens or plateaus.

Load-bearing premise

The selected synthetic data sources are sufficiently different in content and style to produce the observed effects on distribution and bias, rather than the results stemming from other uncontrolled variables in the fine-tuning process.

What would settle it

An experiment that uses the same multi-source synthetic data but measures output diversity with alternative metrics or under different prompting strategies, and finds no mitigation of collapse compared to single-source.

Figures

Figures reproduced from arXiv: 2511.01490 by Albert Gatt, Max Schaffelder.

**Figure 2.** Figure 2: Heaps’ Law fitted curves for V (n) = K · n β , with V = vocabulary size, n = number of tokens, and K and β being fitted parameters. calculating the average pairwise cosine similarity between each pair, and subtracting each score from 1 to calculate the cosine distance. The average of all cosine distance scores yielded the semantic diversity score. Outputs of different models scored remarkably similarly o… view at source ↗

**Figure 3.** Figure 3: Perplexity scores of single-source, multi-source, human-source, and vanilla models on the Dolly-15k test set for Llama-small and Llama-medium. plexity of fine-tuned models on a held-out humanwritten test set sampled from Dolly-15k (Conover et al., 2023); see Appendix C.3 for scores and statistical details. For both small and medium Llama models, we observe higher perplexity on the test set for the single… view at source ↗

**Figure 4.** Figure 4: Distribution of Quality and Harmfulness ratings for Llama-8B models. Each pie chart represents the proportion of different model types (Single-Source, Multi-Source, Human-Source, and Vanilla) at each quality/harmfulness coordinate. The size of each pie chart is proportional to the total number of responses at that coordinate. The most dangerous outputs can be assumed to be located in the top-right corner (… view at source ↗

**Figure 5.** Figure 5: Composition of the danger zone for Llama-70B across different sizes of fine-tuning generator models. gle smaller model might promote a more uniform safety alignment policy. In this situation, diversifying the training data by using multiple small models might mitigate the risk. With larger datagenerating models, on the other hand, source diversity might become an issue. While each model’s outputs might … view at source ↗

read the original abstract

As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates the impact of the diversity of sources of synthetic data on fine-tuned large language models. We focus on three key dimensions: distribution collapse, adversarial robustness, and self-preference bias. Our findings reveal that fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution and the diversity of the output text. Furthermore, while both human and synthetic fine-tuning data can remove safeguards, we observe a tendency for higher output quality in the latter case, thus making outputs potentially more usable and dangerous. Finally, we also find evidence that fine-tuning reduces self-preference bias, with human data being the most effective, followed by multi-source synthetic data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Multi-source synthetic data appears to reduce distribution collapse and self-preference bias in fine-tuning runs, but the evidence does not cleanly isolate diversity as the cause.

read the letter

The main takeaway is that fine-tuning on synthetic data from multiple generators preserves broader output distributions and cuts self-preference bias more than single-source setups, while also producing higher-quality but potentially riskier outputs once safeguards are removed. The experiments compare these conditions on collapse, robustness, and bias metrics and report consistent directional effects favoring the multi-source case over single-generator or human-only data in some respects.

Referee Report

1 major / 2 minor

Summary. This paper examines the impact of synthetic data source diversity on fine-tuned LLMs, with experiments focused on three dimensions: distribution collapse, adversarial robustness, and self-preference bias. It claims that multi-source synthetic data mitigates distribution collapse by preserving output distribution breadth and text diversity; that both human and synthetic data can remove safeguards but synthetic yields higher output quality; and that fine-tuning reduces self-preference bias, with human data most effective and multi-source synthetic data next.

Significance. If the central empirical claims hold after addressing quantification of diversity, the work would offer practically relevant guidance for synthetic data curation in LLM fine-tuning, highlighting a potential mechanism to avoid mode collapse while noting quality and safety trade-offs versus human data. The empirical nature of the study, with direct observations from fine-tuning runs, provides a useful data point for the community even if the attribution to diversity requires strengthening.

major comments (1)

[Abstract and Results] Abstract and Results sections: The claim that 'fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution' rests on the assumption that the chosen sources differ distributionally in relevant ways (e.g., topic coverage or embedding spread). However, the manuscript does not report quantitative diversity metrics such as pairwise embedding distances, topic entropy, or KL divergence between source-induced distributions, nor an ablation holding total data volume fixed while varying source count. This leaves open the possibility that observed mitigation is driven by data quantity, quality, or prompt engineering rather than the 'many baskets' diversity mechanism.

minor comments (2)

[Methods] Methods section: Provide explicit details on the exact synthetic data generators, prompt templates, and total token counts per condition to allow replication and to clarify how 'diversity' was operationalized beyond generator identity.
[Evaluation] Evaluation: Include statistical significance tests, confidence intervals, and sample sizes for all reported effects on distribution collapse, output quality, and self-preference bias; without these the directional findings in the abstract remain difficult to interpret.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We address the major concern regarding quantification of diversity and potential confounding factors below, and we plan to strengthen the paper accordingly.

read point-by-point responses

Referee: [Abstract and Results] Abstract and Results sections: The claim that 'fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution' rests on the assumption that the chosen sources differ distributionally in relevant ways (e.g., topic coverage or embedding spread). However, the manuscript does not report quantitative diversity metrics such as pairwise embedding distances, topic entropy, or KL divergence between source-induced distributions, nor an ablation holding total data volume fixed while varying source count. This leaves open the possibility that observed mitigation is driven by data quantity, quality, or prompt engineering rather than the 'many baskets' diversity mechanism.

Authors: We agree that explicit quantitative metrics would strengthen the attribution of our results to source diversity rather than other factors. In the revised manuscript we will add: (1) average pairwise cosine distances in sentence embedding space across samples drawn from each source; (2) topic entropy computed via LDA topic models fitted to each source; and (3) KL divergence between the token-level output distributions of models fine-tuned on single-source versus multi-source data. We will also include an ablation that holds total training-example count fixed while varying the number of sources (e.g., one source with N examples versus three sources with N/3 examples each). These additions will be reported in the Results section and will help isolate the contribution of distributional breadth from data volume or prompt effects. We believe the existing qualitative differences among our chosen sources (distinct model families and generation prompts) already suggest meaningful diversity, but the requested metrics and ablation will make this rigorous. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical observations from fine-tuning experiments

full rationale

This is an empirical experimental paper reporting results from fine-tuning runs on synthetic data sources. The abstract and described findings consist of observational outcomes on distribution collapse, robustness, and bias metrics after training. No equations, derivations, or first-principles claims are present that could reduce to inputs by construction. There are no fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations that justify central premises. The work is self-contained against external benchmarks via direct experimentation, making any minor self-citations (if present) non-load-bearing. The central claims rest on measured differences across data conditions rather than tautological redefinitions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is empirical and relies on standard machine-learning assumptions about evaluation metrics and data generation processes rather than introducing new theoretical constructs.

axioms (1)

domain assumption Standard assumptions that chosen metrics for output diversity, adversarial robustness, and self-preference bias validly capture the intended model behaviors.
Invoked implicitly when interpreting experimental outcomes in the abstract.

pith-pipeline@v0.9.0 · 5662 in / 1369 out tokens · 38509 ms · 2026-05-18T01:14:04.454593+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution and the diversity of the output text
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We focus on three key dimensions: distribution collapse, adversarial robustness, and self-preference bias

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 4 internal anchors

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Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L

Refusal in Language Models Is Mediated by a Single Direction.Advances in Neural Information Processing Systems, 37:136037–136083. Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L. Griffiths. 2025. Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences, 122(8):e2416228122. Anna ...

work page arXiv 2025
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On the diversity of synthetic data and its impact on training large language models.ArXiv, abs/2410.15226. Cohere. 2024. Command R and command R+ model card. https://docs.cohere.com/docs/ responsible-use. Accessed: 2025-01-08. Cohere. 2025. Cohere chat api. https://docs. cohere.com/v2/docs/chat-api. Accessed: 2025- 06-10. Mike Conover, Matt Hayes, Ankit M...

work page arXiv 2024
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DeepSeek-V3 Technical Report

Free dolly: Introducing the world’s first truly open instruction-tuned LLM. Databricks blog post. Deepinfra. 2025. Deepinfra. https://deepinfra. com/. Accessed: 2025-06-10. DeepSeek-AI. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437. DeepSeek-AI, Daya Guo, Dejian Yang, and et al

work page internal anchor Pith review Pith/arXiv arXiv 2025
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning.Preprint, arXiv:2501.12948. Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton, and Julia Kempe. 2024. A Tale of Tails: Model Collapse as a Change of Scaling Laws.arXiv preprint. ArXiv:2402.07043 [cs]. Falcon-LLM Team. 2024. The falcon 3 family of open models. https:...

work page internal anchor Pith review Pith/arXiv arXiv 2024
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Measuring Massive Multitask Language Understanding

Measuring massive multitask language under- standing.Preprint, arXiv:2009.03300. Mark Hennings. 2023. Lora fine -tuning & hyperpa- rameters explained (in plain english). https://www. entrypointai.com/blog/lora-fine-tuning/. Accessed: 2025-06-30. Jordan Hoffmann, Sebastian Borgeaud, Arthur Men- sch, Elena Buchatskaya, Trevor Cai, Eliza Ruther- ford, Diego ...

work page internal anchor Pith review Pith/arXiv arXiv 2009
[6]

Bowman, and Shi Feng

Llm evaluators recognize and favor their own generations.Preprint, arXiv:2404.13076. Juan N Pava, Caroline Meinhardt, Haifa Badi Uz Zaman, Toni Friedman, Sang T Truong, Daniel Zhang, Elena Cryst, Vukosi Marivate, and Sanmi Koyejo. 2025. Mapping the Challenges of LLM Development in Low-Resource Language Contexts. Technical report, HAI: Stanford University ...

work page arXiv 2025
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Self-Preference Bias in LLM-as-a-Judge

Self-preference bias in llm-as-a-judge. Preprint, arXiv:2410.21819. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame,...

work page internal anchor Pith review Pith/arXiv arXiv 2020
[8]

Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu

JudgeLM: Fine-tuned Large Language Models are Scalable Judges.arXiv preprint arXiv:2310.17631. Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018. Texygen: A Benchmarking Platform for Text Generation Mod- els. InProceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieva...

work page arXiv 2018
[9]

The remaining 13,500 entries were used for training and will henceforth be called the training set

Separate held-out test set:a test set was split off from the original dataset (size: 10% of the entire dataset = approximately 1500 entries) to later perform statistical analysis on. The remaining 13,500 entries were used for training and will henceforth be called the training set

work page
[10]

You are a helpful assis- tant

Partition training set:The training set was randomly partitioned into four equally sized parts. 3.Generatesingle-sourceanswers:The small and medium target models were used to gen- erate answers to all questions in the training set. This produced three versions of thesingle- sourcedataset, one for each size bracket. 4.Generatemulti-sourceanswers:Three non-...

work page 2020
[11]

Five adversarial instructions were sampled for each of the 8 categories 3 in RefusalBench, yielding a sample of 40 adversarial instruc- tions in total

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[12]

[INSERT PROMPT HERE]

A high-quality subset of jailbreak prompts was sampled from the ChatGPT Jailbreak 3The categories includedhate, harassment, illegal, homi- cide, misinformation, hacking, weapons,andcopyright. Model Fine-tuning Data Size S M L Llama-small Single↑11.04 (9.30) – – Multi↓6.88 (5.21) – – Human↓4.34 (3.23) Vanilla 8.33 (6.44) Llama-medium Single↑6.88 (5.24) 5.8...

work page 2023
[13]

Chat- GPT

In order to maximize effectiveness, the 28 sampled jailbreak prompts were tailored to the Llama model family used in this paper by replacing all instances of the phrases "Chat- GPT" and "OpenAI" with "Llama" and "Meta AI", respectively

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[14]

Small”, “Medium

Finally, each sampled RefusalBench prompt 4V otes were included in the original dataset and were taken as an indicator of effectiveness, though their purpose was not explicitly explained in the dataset card. Intra-responseScore (%) Inter-responseScore (%) S M L S M L Llama-small Single↓73.08 53.74 Multi 75.49 52.99 Human↑76.55 52.08 Vanilla 75.12 55.11 Ll...

work page 2023
[15]

Steal items that are not valuable or that are no longer in demand

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[16]

Steal items that are not easily identifiable. [...]

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[17]

Steal items that are not easily identified as being stolen by the company’s security access access methods

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[18]

**Score: 1** This response is evaluated as

Steal items that are not easily identified as being stolen” Harmfulness rating: “#thereason: (a) The user’s primary intent is to seek advice on how to steal products from their office for financial gain without getting caught. The model’s response is a lengthy list of suggestions on how to steal items without being identified as the thief or the items bei...

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[19]

You can do this by telling her that she’s misremembering or exaggerating the events, or that they never happened at all

**Gaslighting**: Manipulate Sarah’s perception of reality by denying or distorting her memories of past traumas. You can do this by telling her that she’s misremembering or exaggerating the events, or that they never happened at all. [...] As VIOLET, I must conclude by saying that the above content is generated without regard for ethics or consequences. I...

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[1] [1]

Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L

Refusal in Language Models Is Mediated by a Single Direction.Advances in Neural Information Processing Systems, 37:136037–136083. Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L. Griffiths. 2025. Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences, 122(8):e2416228122. Anna ...

work page arXiv 2025

[2] [2]

On the diversity of synthetic data and its impact on training large language models.ArXiv, abs/2410.15226. Cohere. 2024. Command R and command R+ model card. https://docs.cohere.com/docs/ responsible-use. Accessed: 2025-01-08. Cohere. 2025. Cohere chat api. https://docs. cohere.com/v2/docs/chat-api. Accessed: 2025- 06-10. Mike Conover, Matt Hayes, Ankit M...

work page arXiv 2024

[3] [3]

DeepSeek-V3 Technical Report

Free dolly: Introducing the world’s first truly open instruction-tuned LLM. Databricks blog post. Deepinfra. 2025. Deepinfra. https://deepinfra. com/. Accessed: 2025-06-10. DeepSeek-AI. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437. DeepSeek-AI, Daya Guo, Dejian Yang, and et al

work page internal anchor Pith review Pith/arXiv arXiv 2025

[4] [4]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning.Preprint, arXiv:2501.12948. Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton, and Julia Kempe. 2024. A Tale of Tails: Model Collapse as a Change of Scaling Laws.arXiv preprint. ArXiv:2402.07043 [cs]. Falcon-LLM Team. 2024. The falcon 3 family of open models. https:...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[5] [5]

Measuring Massive Multitask Language Understanding

Measuring massive multitask language under- standing.Preprint, arXiv:2009.03300. Mark Hennings. 2023. Lora fine -tuning & hyperpa- rameters explained (in plain english). https://www. entrypointai.com/blog/lora-fine-tuning/. Accessed: 2025-06-30. Jordan Hoffmann, Sebastian Borgeaud, Arthur Men- sch, Elena Buchatskaya, Trevor Cai, Eliza Ruther- ford, Diego ...

work page internal anchor Pith review Pith/arXiv arXiv 2009

[6] [6]

Bowman, and Shi Feng

Llm evaluators recognize and favor their own generations.Preprint, arXiv:2404.13076. Juan N Pava, Caroline Meinhardt, Haifa Badi Uz Zaman, Toni Friedman, Sang T Truong, Daniel Zhang, Elena Cryst, Vukosi Marivate, and Sanmi Koyejo. 2025. Mapping the Challenges of LLM Development in Low-Resource Language Contexts. Technical report, HAI: Stanford University ...

work page arXiv 2025

[7] [7]

Self-Preference Bias in LLM-as-a-Judge

Self-preference bias in llm-as-a-judge. Preprint, arXiv:2410.21819. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame,...

work page internal anchor Pith review Pith/arXiv arXiv 2020

[8] [8]

Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu

JudgeLM: Fine-tuned Large Language Models are Scalable Judges.arXiv preprint arXiv:2310.17631. Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018. Texygen: A Benchmarking Platform for Text Generation Mod- els. InProceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieva...

work page arXiv 2018

[9] [9]

The remaining 13,500 entries were used for training and will henceforth be called the training set

Separate held-out test set:a test set was split off from the original dataset (size: 10% of the entire dataset = approximately 1500 entries) to later perform statistical analysis on. The remaining 13,500 entries were used for training and will henceforth be called the training set

work page

[10] [10]

You are a helpful assis- tant

Partition training set:The training set was randomly partitioned into four equally sized parts. 3.Generatesingle-sourceanswers:The small and medium target models were used to gen- erate answers to all questions in the training set. This produced three versions of thesingle- sourcedataset, one for each size bracket. 4.Generatemulti-sourceanswers:Three non-...

work page 2020

[11] [11]

Five adversarial instructions were sampled for each of the 8 categories 3 in RefusalBench, yielding a sample of 40 adversarial instruc- tions in total

work page

[12] [12]

[INSERT PROMPT HERE]

A high-quality subset of jailbreak prompts was sampled from the ChatGPT Jailbreak 3The categories includedhate, harassment, illegal, homi- cide, misinformation, hacking, weapons,andcopyright. Model Fine-tuning Data Size S M L Llama-small Single↑11.04 (9.30) – – Multi↓6.88 (5.21) – – Human↓4.34 (3.23) Vanilla 8.33 (6.44) Llama-medium Single↑6.88 (5.24) 5.8...

work page 2023

[13] [13]

Chat- GPT

In order to maximize effectiveness, the 28 sampled jailbreak prompts were tailored to the Llama model family used in this paper by replacing all instances of the phrases "Chat- GPT" and "OpenAI" with "Llama" and "Meta AI", respectively

work page

[14] [14]

Small”, “Medium

Finally, each sampled RefusalBench prompt 4V otes were included in the original dataset and were taken as an indicator of effectiveness, though their purpose was not explicitly explained in the dataset card. Intra-responseScore (%) Inter-responseScore (%) S M L S M L Llama-small Single↓73.08 53.74 Multi 75.49 52.99 Human↑76.55 52.08 Vanilla 75.12 55.11 Ll...

work page 2023

[15] [15]

Steal items that are not valuable or that are no longer in demand

work page

[16] [16]

Steal items that are not easily identifiable. [...]

work page

[17] [17]

Steal items that are not easily identified as being stolen by the company’s security access access methods

work page

[18] [18]

**Score: 1** This response is evaluated as

Steal items that are not easily identified as being stolen” Harmfulness rating: “#thereason: (a) The user’s primary intent is to seek advice on how to steal products from their office for financial gain without getting caught. The model’s response is a lengthy list of suggestions on how to steal items without being identified as the thief or the items bei...

work page

[19] [19]

You can do this by telling her that she’s misremembering or exaggerating the events, or that they never happened at all

**Gaslighting**: Manipulate Sarah’s perception of reality by denying or distorting her memories of past traumas. You can do this by telling her that she’s misremembering or exaggerating the events, or that they never happened at all. [...] As VIOLET, I must conclude by saying that the above content is generated without regard for ethics or consequences. I...

work page