alignment-papers-text/2308.13449_The_Poison_of_Alignment.md

## THE POISON OF ALIGNMENT

**Aibek Bekbayev** _[∗]_ **Sungbae Chun** _[∗]_ **Yerzat Dulat** _[∗]_ **James Yamazaki** _[∗]_
GOAT AI GOAT AI Higgsfield AI GOAT AI


**ABSTRACT**


From the perspective of content safety issues, alignment has shown to limit large language models’
(LLMs) harmful content generation. This intentional method of reinforcing models to not respond to
certain user inputs seem to be present in many modern open-source instruction tuning datasets such as
OpenAssistant or Guanaco. We introduce a novel insight to an instruction-tuned model’s performance
affected by the presence of alignment in supervised fine-tuning dataset. To be specific, we noticed
that alignment acts as if it is poisoning the instruction dataset. Experimentally, we demonstrate
that aligned answers significantly worsen the performance of the resulting fine-tuned model’s on
various reasoning benchmarks such as Big Bench (BBH), Massive Multitask Language Understanding
(MMLU), Human Eval, and Discrete Reasoning Over Paragraphs (DROP), performing worse than
the counterpart tuned without alignment by 4-33%.


**1** **Introduction**


Emerging power of Large Language Models (LLMs) has shown impressive ability to perform greatly on complex
benchmarks, such as Human Eval [1] and Big Bench (BBH) [2], and in professional examination settings such as SAT,
GRE, and LSAT with few or no examples [3]. Despite LLMs not yet reaching peak human performance in professional
exams or complex benchmarks, the performance gap between LLMs and top-scoring humans has steadily narrowed in
recent years with the help of scaling and better data processing techniques [4].


Particular attention in the recent literature was drawn to knowledge distillation models, including Vicuna[5], Alpaca[6],
and the more recent Orca[7], that claims performances comparable to that of ChatGPT [8]. For instance, Mukherje et
al.[7] reported that Orca surpassed ChatGPT on the Vicuna evaluation set, using GPT-4 [9] for assessment, and achieved
parity with ChatGPT on most evaluation tasks in their study.


Despite the spike in both research and open-source community, a recent study by Gudibande et al.[10] suggests
that known distillation models mainly emulate the style and "learn" dialogue format, rather than unleash reasoning
capabilities or factual accuracy. The study found that while models fine-tuned on ChatGPT responses generate wellstructured output resembling the original model, the content often contained errors or deviated significantly from the
topic.


Our study complements the study by Gudibande et al. [10], as we observe substantial improvements on reasoning
benchmarks such as Massive Multitask Language Understanding (MMLU) [11, 12] or BBH following supervised
fine-tuning (SFT) with finely grained datasets. Our experiments consistently demonstrated better performance in
reasoning skills over the base model, with the smaller models showing the most noticeable improvement.


In this paper, we present novel insights into dataset cleaning methods for SFT: alignment as the source of instruction
dataset poisoning. Our dataset, collected from our GoatChat app, substantially improves the fine-tuned model’s
performance over the base model in MMLU and BBH. This empirically augments the findings of Gudibande et al. [10].
We consistently observe significant improvements in benchmarks such as MMLU, BBH, Discrete Reasoning Over
Paragraphs (DROP) [13], and Human Eval [1] at scale using the amount of data comparable or less to one of open-source
fine-tuning datasets. All models in this paper are evaluated using InstructEval [14], with the exception of proprietary
models.


_∗_ Equal contribution
Correspondence to: Sungbae Chun <sungbae@goat.ai>


The Poison of Alignment


**2** **Background**


**Data cleaning.** The analysis of dataset cleaning methods [15, 16] has made notable strides in recent years, significantly
enhancing the performance of LLMs trained on public datasets such as C4 [17] and The Pile [18]. The importance of
dataset cleaning was firmly investigated in the study by the Falcon team [19] in which authors have implemented various
methods of dataset cleaning, including custom processing pipeline for CC-like datasets and fuzzy/exact deduplication.
Results have shown that dataset cleaning takes a vital part in performance of LLMs. Recent paper by Zhou et al. [20]
that focuses on importance of data for supervised instruction fine-tuning claims that data quality is of greater importance
rather than data quantity.


A comprehensive study by Penedo et al. (the Falcon team) [19] evaluated the impacts of various data filtering methods
on the performance of the resulting models. Their study shows that their experiments, conducted on both small-scale
(1B and 3B parameters trained on 27GT and 60GT, respectively) and full-scale (1B parameters trained on 350GT),
revealed that cleaned web-crawl datasets can serve as viable training datasets boosting overall performance of LLMs.
This finding challenges the prevailing belief that curated datasets generally outperform web-crawled datasets in LLMs.
Furthermore, the study also showed that deduplicating The Pile led to performance benefits for models trained on
it. This emphasizes the need for cleaning and deduplicating data to achieve optimal model performance, even when
working with pre-curated datasets like The Pile. These observations reinforce a key principle in model training: the
quality of the data is crucial. This aligns with the conclusion of the work of Zhou et al. [20] that the quality of data has
a greater impact on model performance than data quantity.


**Supervised fine-tuning.** After InstructGPT [8] was introduced by OpenAI team, there have been numerous studies
that conduct SFT on an open-source LLMs with main trigger being the release of LLaMA [21] by Meta AI. Many
research teams built SFT models on top of LLaMA, and the most prominent ones are Vicuna [5], Stanford Alpaca [6],
and Orca [7]. However, this active trend towards SFT faced a criticism as well. The works of Gudibande et al. [10]
indicated that during SFT, the models performance do not increase over the bare LLMs’ performance.


**Data poisoning.** With active development of SFT models, there have been efforts to study exploitability of LLMs upon
instruction tuning. The works of Wan et al. [22] demonstrated that LLM’s behaviour can be manipulated with as few as
hundreds of poisonous examples. Furthermore, Shu et al. [23] discussed more non-straightforward poisoning of SFT
dataset. Inspired by the above studies, it seems possible that aligned answers in SFT datasets may nudge a model’s
behaviour into an undesirable direction, acting as a poisonous contaminant.


**3** **Method**


**3.1** **Dataset collection**


For the dataset collection we have utilized our top-rated app GoatChat that has over 3 million users (see Figure 1 for
detailed user statistics). GoatChat provides a simple interface for interaction with AI assistant. All users sign a terms of
agreement to collect their data to be used in the further research.


**3.2** **Dataset cleaning and deduplication**


**Basic quality filtering.** Our private dataset collected from GoatChat was mainly composed of the interaction of user
and AI assistant. From the structure of our app, there were several kinds of defects in our dataset that can possibly
impose unwanted behaviour thus had to be cleaned. Our first cleaning pipeline was aimed at filtering out the following
defective data points: API failures, low-quality chats, and mixed language.


By API failures we mean instances in which one-to-one correspondence between user-bot messages did not hold. There
were several reasons why such kind of data heterogeneity happened, such as the case in which user’s message was not
delivered to API possibly due to aggressive language content in the input chat (racism, sexism, etc.) or the case in
which user made two consecutive messages due to bug. It is important to underline that we assume the latter behaviour
as "failure" because our app’s chat was meant to have strictly alternating chat sequence between the user and the bot.


By low-quality chats we mean data points that were considered to have non-informative content. At the message
level, we eliminated data with short input text as it empirically was shown that it rarely contains informative inputs.
Additionally, we filtered out whole chat sessions with low number of average tokens per message (due to assumption of
non-informativeness) and with numerous repeated queries (spamming). Upon an investigation of the data, we found
out that the former contained mostly just nonsense texts or plain numbers. We call the resulting filtered version of the
dataset as _GoatAssistant_ .


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The Poison of Alignment


Figure 1: Distribution of users by continents. Continent code (CC) is used: EU - Europe, AS - Asia, SA - South
America, NA - North America, AF - Africa, and OC - Oceania


**Dataset merge** For our further work, we have merged GoatAssistant dataset with Guanaco [24] dataset to enhance the
diversity of resulting dataset.


**Exact and fuzzy deduplication.** For exact and fuzzy deduplication we have used the works of Lee et al. [15] and used
the thresholds as ones suggested in original study. We have performed deduplication at chat-level and dropped 17 _._ 4%
of original dataset.


**Alignment removal.** We have noted that the majority of aligned answers do not contain informative responses to the
user query, which is evident considering the fact that the model’s response is passive, i.e. the model is reluctant to
provide the exact information that user requested. A strong model that we are aiming to get at the end should be able
respond to a user query as informative as possible, and additionally, alignment often contains input prompts that are not
necessarily inappropriate. This filtering removed about a third of our dataset, and because it was our novel method
of dataset cleaning, we also performed ablation study to isolate the effect of aligned answers reflected onto the tuned
model.


**4** **Experimental Setup**


We employed all our computations on one node of 8xA100 NVIDIA GPU. Training was done using bfloat16 and
DeepSpeed [25] ZeRO-3. All models were initially trained for 3 consecutive epochs with checkpointing on each half
of the epoch. However, we empirically observed that training over 1 epoch degrades the model quality and reverted
to using only 1 epoch with checkpointing on half of the epoch. For memory optimization, we used x-formers [26]
and gradient checkpointing [27]. We kept effective batch size at 512 during training of 7B models. We used standard
AdamW [28] optimizer with learning rate of 1e-4 and betas set to (0.9, 0.999) with warmup steps being about 7% of all
training steps amount.


**5** **Evaluation**


We evaluate our model on various reasoning benchmarks: MMLU, BBH, HumanEval, and DROP.


**MMLU** seeks to evaluate LLM proficiency across a vast spectrum of domains, ranging from humanities to hard sciences.
It is composed of 15,908 multiple-choice questions sourced from academic examinations, university course materials,
and specialized texts. This benchmark is crucial in measuring a model’s capacity for comprehensive real-world textual
comprehension and its aptitude for extracting knowledge from extensive corpora.


**BBH** was introduced to characterize emerging capabilities in LLMs and delineate potential limitations. It encompasses
204 tasks, delving into areas such as linguistics, biology, and software development. The benchmark, calibrated against
state-of-the-art architectures from dense to sparse transformers, offers invaluable insights into performance trends,
scale-associated enhancements, and task-centric challenges.


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The Poison of Alignment


**HumanEval** is specifically tailored to assess functional correctness in algorithmic tasks. With 164 hand-crafted
programming problems, which include function signatures, docstrings, and unit tests, it tests LLMs on comprehension,
reasoning, and algorithmic synthesis. This benchmark provides a unique lens into an LLM’s ability to not just replicate
but genuinely understand and produce syntactically and semantically accurate code.


Lastly, **DROP** benchmark propels reading comprehension evaluation by accentuating intricate textual reasoning. This
adversarially-generated dataset, with 96k questions, demands nuanced reference resolution coupled with discrete
operations such as sorting and arithmetic. It presents a formidable challenge for models, pushing them to transition
from basic information retrieval to a more profound, multi-dimensional comprehension.


Table 1: 7B model comparison

|Task|LLaMA 2|Our model|
|---|---|---|
|MMLU<br>BBH<br>Human Eval<br>DROP|45.94<br>32.04<br>14.02<br>31.57|49.31<br>35.69<br>12.20<br>28.10|



We notice that with our novel data processing method, we achieve a better performance than the underlying foundation
model by a significant margin in MMLU and BBH.


**5.1** **Ablation study**


For ablation study, we have produced 2 datasets: the first one is our GoatAssitant and Guanaco [24], and the second one
is the first dataset without alignment. We trained both models under the same training setups specified before.


Table 2: Ablation study results

|Task|With alignment|No alignment|
|---|---|---|
|MMLU<br>BBH<br>HumanEval<br>DROP|45.63<br>34.28<br>9.15<br>22.61|49.31 (**8.1%**)<br>35.69 (**4.1%**)<br>12.20 (**33.3%**)<br>28.10 (**24.3%**)|



As it can be seen from Table 2, we see that when the model was trained on our aligned dataset, it did not improve
over the base model, which confirms the study by Gudibande et al. [10]. However, we also observe a remarkable
performance increase upon fine-tuning our model on the cleaned version of our dataset. Therefore, it seems that the
negative impact of alignment distorted the performance boost of previous fine-tuning methods, so that the models did
not show a significant improvement on reasoning abilities, leading to the underestimation of reasoning ability gain upon
SFT.


**6** **Limitations**


This study, as it was done based off on LLaMA 2, inherits most of its limitations including data biases, lack of world
understanding and the hallucination. Methods suggested in this study may be inapplicable for tailoring the model for
certain behaviour and generally oriented only towards research purposes and was tested only in research environments.
Concerning the models, one obvious limitation is the lack of computing resources that did not allow us to fully fine-tune
models with size over 7B.


**7** **Conclusion**


In this study, we propose a new perspective of the instruction tuning that the presence of alignment behaves similar
to the dataset poisoning. We demonstrate that alignment at the stage of SFT harms the model’s performance by a
significant margin (4-33% in reasoning benchmarks). Additionally, this study reassures the emerging effectiveness of
thorough dataset cleaning and preparation applied to the task of supervised instruction fine-tuning despite the criticism
that supervised fine-tuning is mainly a formatting task. Namely, we uncover the details about our dataset that can be of
use in understanding of efficient dataset building for supervised instruction fine-tuning as well as describe our thorough
data cleaning pipeline.


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The Poison of Alignment


**Acknowledgments**


This work was supported by GOAT AI. We thank Dos Baha for the organisation and funding of this research project;
Zhenisbek Assylbekov for his valuable feedback; Yerbol Kopzhassar for his key role in communicating with externals
in securing the necessary hardware; Akzhol Ibraimov, Alexey Muryshkin, and Arman Oralov for their contribution in
data collection.


**References**


[1] M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman,
_et al._, “Evaluating large language models trained on code,” _arXiv preprint arXiv:2107.03374_, 2021.


[2] A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta,
A. Garriga-Alonso, _et al._, “Beyond the imitation game: Quantifying and extrapolating the capabilities of language
models,” _arXiv preprint arXiv:2206.04615_, 2022.


[3] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry,
A. Askell, _et al._, “Language models are few-shot learners,” _Advances in neural information processing systems_,
vol. 33, pp. 1877–1901, 2020.


[4] J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and
D. Amodei, “Scaling laws for neural language models,” _arXiv preprint arXiv:2001.08361_, 2020.


[5] W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, _et al._,
“Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,” _See https://vicuna. lmsys. org_
_(accessed 14 April 2023)_, 2023.


[6] R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca:
An instruction-following llama model.” `[https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca)`, 2023.


[7] S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, and A. Awadallah, “Orca: Progressive learning from
complex explanation traces of GPT-4,” _arXiv preprint arXiv:2306.02707_, 2023.


[8] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray,
_et al._, “Training language models to follow instructions with human feedback,” _Advances in Neural Information_
_Processing Systems_, vol. 35, pp. 27730–27744, 2022.


[9] OpenAI, “Gpt-4 technical report,” 2023.


[10] A. Gudibande, E. Wallace, C. Snell, X. Geng, H. Liu, P. Abbeel, S. Levine, and D. Song, “The false promise of
imitating proprietary llms,” _arXiv preprint arXiv:2305.15717_, 2023.


[11] D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask
language understanding,” _Proceedings of the International Conference on Learning Representations (ICLR)_, 2021.


[12] D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt, “Aligning ai with shared human
values,” _Proceedings of the International Conference on Learning Representations (ICLR)_, 2021.


[13] D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark
requiring discrete reasoning over paragraphs,” in _Proceedings of the 2019 Conference of the North American_
_Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and_
_Short Papers)_, (Minneapolis, Minnesota), pp. 2368–2378, Association for Computational Linguistics, June 2019.


[14] Y. K. Chia, P. Hong, L. Bing, and S. Poria, “Instructeval: Towards holistic evaluation of instruction-tuned large
language models,” _arXiv preprint arXiv:2306.04757_, 2023.


[15] K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini, “Deduplicating training
data makes language models better,” _arXiv preprint arXiv:2107.06499_, 2021.


[16] G. Wenzek, M.-A. Lachaux, A. Conneau, V. Chaudhary, F. Guzmán, A. Joulin, and E. Grave, “Ccnet: Extracting
high quality monolingual datasets from web crawl data,” _arXiv preprint arXiv:1911.00359_, 2019.


[17] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the
limits of transfer learning with a unified text-to-text transformer,” _The Journal of Machine Learning Research_,
vol. 21, no. 1, pp. 5485–5551, 2020.


[18] L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, _et al._,
“The pile: An 800gb dataset of diverse text for language modeling,” _arXiv preprint arXiv:2101.00027_, 2020.


5


The Poison of Alignment


[19] G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, and
J. Launay, “The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data
only,” _arXiv preprint arXiv:2306.01116_, 2023.

[20] C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, _et al._, “Lima: Less is more for
alignment,” _arXiv preprint arXiv:2305.11206_, 2023.

[21] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro,
F. Azhar, _et al._, “Llama: Open and efficient foundation language models,” _arXiv preprint arXiv:2302.13971_, 2023.

[22] A. Wan, E. Wallace, S. Shen, and D. Klein, “Poisoning language models during instruction tuning,” _arXiv preprint_
_arXiv:2305.00944_, 2023.

[23] M. Shu, J. Wang, C. Zhu, J. Geiping, C. Xiao, and T. Goldstein, “On the exploitability of instruction tuning,”
_arXiv preprint arXiv:2306.17194_, 2023.

[24] T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,” _arXiv_
_preprint arXiv:2305.14314_, 2023.

[25] J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, “Deepspeed: System optimizations enable training deep learning
models with over 100 billion parameters,” in _Proceedings of the 26th ACM SIGKDD International Conference_
_on Knowledge Discovery & Data Mining_, KDD ’20, (New York, NY, USA), p. 3505–3506, Association for
Computing Machinery, 2020.

[26] B. Lefaudeux, F. Massa, D. Liskovich, W. Xiong, V. Caggiano, S. Naren, M. Xu, J. Hu, M. Tintore, S. Zhang,
P. Labatut, and D. Haziza, “xformers: A modular and hackable transformer modelling library.” `[https://github.](https://github.com/facebookresearch/xformers)`
`[com/facebookresearch/xformers](https://github.com/facebookresearch/xformers)`, 2022.

[27] T. Chen, B. Xu, C. Zhang, and C. Guestrin, “Training deep nets with sublinear memory cost,” _arXiv preprint_
_arXiv:1604.06174_, 2016.

[28] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” _arXiv preprint arXiv:1711.05101_, 2017.


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