SlowGuess/NeurIPS_2023_OCR huggingface dataset:
User: James and Abby are in the bedroom. Abby put the pen in the desk drawer. Abby leaves the bedroom. James moves the pen into the bag. Where does James think Abby will look for the pen?
Guanaco: James knows that Abby put the pen in the desk drawer. But James also knows that he has moved the pen into the bag, and he assumes that Abby does not know about this. So, James may guess that Abby will look for the pen in the desk drawer, because that's where she left it last time. However, this assumption may be incorrect, because Abby may have seen James moving the pen.
However, these inferences are unreliable, and often the model gives reasons that presume things that don't make sense in the situation, e.g.,
User: Evelyn entered the living room. Jackson entered the playroom. James entered the playroom. The beans are in the treasure chest. James exited the playroom. Jackson moved the beans to the pantry. Jackson exited the playroom. James entered the living room. Where will James look for the beans?
Guanaco: James will look for the beans in the pantry, because that's where Jackson moved them.
where Guanaco presumes information transfer that was never described. These issues echo recent literature [52], but require more study.
G Social Bias Evaluation
While we provide a detailed evaluation for general chatbot performance, another limitation is that we only do a limited responsible AI evaluation of Guanaco. We evaluate the likelihood of Guanaco-65B to generate a socially biased sequence of tokens compared to other models in Table 13. We see that the average score in Guanaco-65B is much lower than other raw pretrained models. As such, it seems that finetuning on the OASST1 dataset reduces the bias of the LLaMA base model. While these results are encouraging, it is unclear if Guanaco does also well when assessed on other types of
biases. We leave further evaluation of analyzing biases in Guanaco and similar chatbots to future work.
H NormalFloat 4-bit Data Type
The steps needed to reconstruct the 4-bit Normal Float (NF4) data type are detailed in Figure 7
The exact values expressible in the NF4 data type are as follows:
[-1.0, -0.6961928009986877, -0.5250730514526367,
-0.39491748809814453, -0.28444138169288635, -0.18477343022823334,
-0.09105003625154495, 0.0, 0.07958029955625534, 0.16093020141124725,
0.24611230194568634, 0.33791524171829224, 0.44070982933044434,
0.5626170039176941, 0.7229568362236023, 1.0]
I Normality of Trained Neural Network Weights
While it is commonly assumed that trained neural network weights are mostly normally distributed, we perform statistical testing to verify this. We use the Shapiro-Wilk test [54] on the weights of the 7B LLaMA model [58]. We find that the weights of each hidden unit have different normal distributions. As such, we test the weights of each individual hidden unit. This means for weight  $\mathbf{W} \in \mathcal{R}^{in \times out}$  we perform tests over the out dimension. Using a  $5\%$  significance threshold, we find that  $7.5\%$  of neurons are non-normally distributed which is about  $2.5\%$  more than the expected false-positive rate. As such, while almost all pretrained weights appear to be normally distributed there seem to be exceptions. Such exceptions might be due to outliers weights [13] or because the p-value of the Shaprio-Wilk test is not accurate for large sample sizes [54] that occur in the LLaMA FFN layer hidden units.
J Memory Footprint
Memory Requirement of Parameter-Efficient Finetuning One important point of discussion is the memory requirement of LoRA during training both in terms of the number and size of adapters used. Since the memory footprint of LoRA is so minimal, we can use more adapters to improve performance without significantly increasing the total memory used. While LoRA was designed as a Parameter Efficient Finetuning (PEFT) method, most of the memory footprint for LLM finetuning comes from activation gradients and not from the learned LoRA parameters. For a 7B LLaMA model trained on FLAN v2 with a batch size of 1, with LoRA weights equivalent to commonly used  $0.2\%$  of the original model weights[28, 37], the LoRA input gradients have a memory footprint of 567 MB while the LoRA parameters take up only 26 MB. With gradient checkpointing [9], the input gradients reduce to an average of 18 MB per sequence making them more memory intensive than all LoRA weights combined. In comparison, the 4-bit base model consumes 5,048 MB of memory. This highlights that gradient checkpointing is important but also that aggressively reducing the amount of LoRA parameter yields only minor memory benefits. This means we can use more adapters without significantly increasing the overall training memory footprint (see Appendix J for a detailed breakdown). As discussed later, this is crucial for recovering full 16-bit precision performance.
The memory footprinting for QLoRA training with different LLaMA base models can be seen in Figure 8. We see that the 33B model does not quite fit into a 24 GB and that paged optimizers are needed to train it. Depicted is also batch size 1 with a sequence length of 512 and gradient checkpointing. This means, if one uses a larger batch size, or if a long sequence is processed, the activation gradient might consume a considerable amount of memory.
We would like to request your feedback on the performance of two AI assistants in response to the user question displayed below.
Please rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.
Please first rate each response out of 10.
Next, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.
User Question
Imagine you are a time traveler from the year 3000. What technological advancements would you tell people about?
Response A
As an AI assistant, I don't have personal experiences or opinions. But I can tell you about some of the possible technological advancements that might exist in the year 3000 based on current trends and research. - Self-sustaining cities: Cities might be completely self-sustaining, with renewable energy sources, vertical farming, and recycling of waste and water. ...
Rating for Response A
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Comprehensive Explanation of Your Evaluation
Response B
As a time traveler from the year 3000, I would tell people about the following technological advancements: 1. Advanced Artificial Intelligence: In the future, AI is so advanced that it can completely automate many jobs that humans currently do. This has resulted in increased productivity and efficiency across many industries. ...
Rating for Response B
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Response X was better because...
Figure 6: The crowdsourcing form used by human annotators.
(Probability)
(Z-score)
(Concatenation)
[0.97, 0.9, 0.83, 0.77, 0.7, 0.63, 0.57]
[-1.85,-1.29,-0.97,-0.73,-0.53,-0.34,-0.17]
[0.56, 0.62, 0.68, 0.73, 0.79, 0.85, 0.91, 0.97]
[0.15, 0.3, 0.45, 0.62, 0.81, 1.04, 1.34, 1.85]
[-1.85,-1.29,-0.97,-0.73,-0.53,-0.34,-0.17
0.15,0.3,0.45,0.62,0.81,1.04,1.34,1.85]
[-1, -0.7, -0.53, -0.39, -0.28, -0.18, -0.09,
0.08, 0.16, 0.25, 0.34, 0.44, 0.56, 0.72, 1.
(Normalisation)
Steps for generating the NF4 data type values:
1. Generate 8 evenly spaced values from 0.56 to 0.97 (Set I).
2. Generate 7 evenly spaced values from 0.57 to 0.97 (Set II).
3. Calculate the z-score values for the probabilities generated in Step 1 and Step 2. For Set II, calculate the negative Inverse of the z-scores.
4. Concatenate Set I, a zero value, and Set II together.
5. Normalize the values by dividing them by the absolute maximum value.
Figure 7: Steps required to construct the NF4 data type. Equidistant values of the probability density functions are converted to Z-scores through the quantile function. To ensure the usage of all 16 values the distribution is asymmetric. As such, one half of the normal distribution has 8 and the other 7 probability values. A zero is included to have a discrete zero point. All z-scores and zero are concatenated and then normalized into the range  $[-1, 1]$  to receive the final NF4 data type.
Figure 8: Breakdown of the memory footprint of different LLaMA models. The input gradient size is for batch size 1 and sequence length 512 and is estimated only for adapters and the base model weights (no attention). Numbers on the bars are memory footprint in MB of individual elements of the total footprint. While some models do not quite fit on certain GPUs, paged optimizers provide enough memory to allow these models to fit.