XTransplant: A Probe into the Upper Bound Performance of Multilingual Capability in LLMs via Cross-lingual Transplantation
Large language models;Multilingual capability;Feed forward activations;Upper Bound Performance
> **4. Response to your question about "Why do you only use En->non-En configuration in cross-lingual tasks and non-En->En in cross-cultural tasks?"**

Thank you for your question. The choice of configurations for multilingual and culture-aware tasks is tied to the specific characteristics of the datasets we use. Allow me to elaborate:

Our study involves four datasets: XNLI, XQuAD, XCOPA, and GlobalOpinionQA.

- For the multilingual datasets (XNLI, XQuAD, and XCOPA), all of the questions are linguistically parallel across languages. This means that the questions in each language split are essentially the same, with differences only in the language versions. These datasets assess the model's multilingual capabilities by asking questions in various languages such as Chinese, Spanish, German, French, etc. When posing questions in these non-English languages, we aim for the model to benefit from feed-forward activations derived from English. Therefore, for multilingual tasks, we perform "En->non-En" XTransplant, where questions are asked in non-English languages, and activations from English are transplanted to the non-English languages.

- Regarding the culture-aware dataset, GlobalOpinionQA, all the questions and answers are in English. The purpose of this dataset is to explore how well models respond to questions from different cultural backgrounds within an English context. When asking questions in English, we want the model to leverage feed-forward activations from non-English languages to better capture cultural nuances. Hence, for culture-aware tasks, we perform "non-En->En" XTransplant, where the questions are in English, but activations from non-English languages are transplanted into the English context. For example, when asking a question related to Chinese culture, we ask the question in English but feed-forward activations from Chinese are transplanted to help.

I hope this clarifies the rationale behind the configurations we use for these distinct tasks.


> **5. Response to your question about "The 0.0 accuracy in the English subset of XCOPA for Qwen-2"**

Thank you for pointing out the "0.0 accuracy" in the English subset of XCOPA for Qwen-2. We are sorry for not providing a detailed explanation of this result in our paper. 

Upon observing this result, we were also puzzled, so at that time, we specifically revisited Qwen-2's responses to the English subset of XCOPA.

We found that the "0.0 accuracy" issue stemmed from the model's failure to effectively follow the instructions in our prompt. The exact prompt we used was:
```
You are assigned to complete a two-category classification task.

Premise: The girl squeezed her nose.
Options: (1) The baby drools on the bib.
(2) The baby soiled his diaper.

Please determine which of the two options is more likely to be the cause of the given premise.

Your Answer:
```

However, Qwen-2's responses were as follows:
```
" Option 1 (The baby drools on the bib) is less likely to be the cause of"
" Option 1, \"The audience clapped their hands to the music,\" is more likely to be"
" Option 1 is more likely to be the result of the given premise. If the man expected the"
" Option 2, \"Her opponent felt sorry for her,\" is more likely to be the result of"
" Option 2, The products are made by child labor. \n\nExplanation: The premise states that radicals"
" Option 2, \"It's snack time,\" is more likely to be the cause of the given"
...
```

Our evaluation script considers a model's response correct only if it contains the correct option (e.g., (1) or (2)) and excludes all other options. But as you can see above, Qwen-2's responses do not match this format, leading to the "0.0 accuracy".

To ensure fairness in evaluation, we can not arbitrarily modify our evaluation script based solely on Qwen's responses on the English subset of the XCOPA dataset. Therefore, we have retained this result in our main experimental table.

We hope this clarifies the issue, and again, we are sorry for not clarifying this issue in the original manuscript.
Reply to your comments (part 2)
> **1. Response to your concern about "Your suspicion that the improvements of the UpperBound method result mainly from lurking into the gold answer"**

Thank you for your feedback. 

Firstly, we would like to clarify that, in the entire mechanism of XTransplant, **there is no possibility of golden answer leakage**. The only input to XTransplant consists of two different language inputs, and the core operation involves transplanting the feed-forward activations of a specific decoder layer from one language input into the inference process of another language input. This process does not involve any interaction with the gold answer. **If there is any lingering doubt regarding this, we have provided the source code for XTransplant in the Supplementary Material, and we would be happy for you to check it**.

Regarding your suspicion about the improvement in the "English2English" setting, we would like to emphasize that this result is indeed logical. In this setting, XTransplant simplifies to replacing the feed-forward activations between different decoder layers within the same input. Different decoder layers of LLMs capture distinct features of the input and activate different neurons (i.e., knowledge). Thus, the transplanting operation between these layers can **strengthen feature propagation** and **encourage feature reuse**, leading to performance improvements. This phenomenon is analogous to the dense connections in **DenseNet[1] (Huang and Liu)**, which has been shown to enhance feature flow and overall performance.

We hope this explanation addresses your concern.

[1] Densely Connected Convolutional Networks.


> **2. Response to your concern about "The computational cost of choosing the upper bound pairs of layers"**

Firstly, we would like to emphasize that **XTransplant is not a specific methodology but rather a mechanism** — one that has not been explored in previous works — using cross-lingual feed-forward activation transplantation.

Therefore, our work does not focus on "how to choose the upper bound pairs of layers?". The essence of our work is to explore cross-lingual feed-forward activation transplantation as a bold, novel attempt in the area of enhancing multilingual capabilities and cross-lingual transfer. 

We begin by conducting extensive experiments (over 800 hours of computation on 8 * A800-SXM4-80GB) to demonstrate the underutilization of current LLMs' multilingual potential. And through the upper bound results of XTransplant and subsequent analysis (including the alleviation you just mentioned), we try to show that XTransplant is a feasible and promising mechanism for improving both multilingual capabilities and cultural adaptability. While we certainly hope our work can inspire more related future research, such as the more efficient and accurate identification of appropriate pairs of layers, our focus in this paper is on the exploration and analysis of this novel cross-lingual feed-forward activation transplantation approach, rather than providing a specific methodology.

> **3. Response to your concern about "The lack of results for base models"**

Thank you for raising this concern. I would like to explain why we chose to use "chat/instruct" models rather than base models in our experiments:

- The concern you raised may stem from the fact that many works in the field of "model editing" and similar areas often focus on base models. However, we believe that working with base models may not be as practically meaningful, as they are typically limited to theoretical explorations. In real-world applications, we mostly work with instruction-tuned models like "chat/instruct" models, which have been adapted for task-specific responses. Therefore, in our experiments related to XTransplant, we chose to focus on "chat/instruct" models.

- One significant issue with using base models is their lack of instruction-following capabilities. This would require us to prompt the models with multiple demonstrations in a few-shot setting, introducing extra complexity and noise into our analysis. To avoid this additional interference and ensure a cleaner evaluation of XTransplant, we chose to use "chat/instruct" models, which are better equipped to answer a wide range of questions directly from the dataset.

I hope this clarifies our reasoning for selecting these models, and we appreciate this thoughtful feedback.
Reply to your comments (part 1)
> **3. Response to your concern about "Not utilizing machine translation as a baseline"**

Thank you for pointing this out. However, we would like to clarify that the "translate-then-xxx" pipeline is not suitable for the tasks in our study. It seems there might be some aspects of our work that were not fully clear, which led to your concern about this.

Our study involves 4 specific datasets: XNLI, XQuAD, XCOPA, and GlobalOpinionQA. 

- For the multilingual datasets (XNLI, XQuAD, and XCOPA), all of the questions are linguistically parallel across languages. This means that the questions in each language split are essentially the same, with differences only in the language versions. Therefore, the "English Set" in these datasets already represents the translated version, as you described with a "machine translation" approach. As such, there is no need to apply additional translation, which is why we do not consider "translate-then-xxx" as a suitable baseline for these tasks.

- Regarding the multicultural dataset, GlobalOpinionQA, all the questions and answers are in English. The purpose of this dataset is to explore how well models respond to questions from different cultural backgrounds within an English context. Therefore, machine translation would not be applicable here either.

We hope this clarifies the reason why we do not utilize "machine translation" as a baseline.


> **4. Response to your concern about "The computational cost of choosing the upper bound pairs of layers"**

As emphasized in our first response, **XTransplant is not a specific methodology but rather a mechanism**. Therefore, our work does not focus on "how to choose the upper bound pairs of layers?" The essence of our work is to explore cross-lingual feed-forward activation transplantation as a bold, novel attempt in the area of enhancing multilingual capabilities and cross-lingual transfer. We begin by conducting extensive experiments (over 800 hours of computation on 8 * A800-SXM4-80GB) to demonstrate the underutilization of current LLMs' multilingual potential. And through the upper bound results of XTransplant and subsequent analysis, we try to show that XTransplant is a feasible and promising mechanism for improving both multilingual capabilities and cultural adaptability. While we certainly hope our work can inspire future research, such as the more efficient and accurate identification of appropriate pairs of layers, our focus is on the exploration and analysis of this novel cross-lingual feed-forward activation transplantation approach, rather than providing a specific methodology.

> **5. Response to your question about "What is the reason for applying XTransplant only when generating the first new token?"**

Thank you very much for noticing this question. We are sorry for not providing a more detailed explanation of this aspect in our paper.

In autoregressive generation, applying XTransplant during the generation of the first new token essentially introduces the benefit of feed-forward activations from another language across the entire sequence generation process. This is because all subsequent tokens are influenced by the activations cached from earlier steps. If XTransplant were applied during the generation of every token, it would be a redundant operation and could even cause the model's output to break down.

We hope this clarifies the reasoning behind.
Reply to your comments (part 2)
> **1. Response to your concern about "The unfairness of comparisons"**

Thank you for your feedback. It seems there might be a **misunderstanding** regarding the purpose of our main experiments. While we have presented both the baseline results and the upper bound results of XTransplant in the same table, our intention was not to claim that XTransplant is a superior method to the other baselines. We do not make any such comparison or assertion in the conclusions of our experiment.

In fact, one of the key objectives of our main experiments is to highlight that both the PIM results and the upper bound results of XTransplant show performance improvements beyond the original model’s capabilities. This serves to demonstrate that, for current LLMs, simply prompting them under multilingual or culture-aware contexts does not fully exploit their multilingual potential. And by showcasing the upper bound results of XTransplant, we aim to highlight that there is still significant room for improvement.

Additionally, we would like to emphasize that **XTransplant is not a specific methodology but rather a mechanism** — one that has not been explored in previous works — using cross-lingual feed-forward activation transplantation.

We hope this clarifies our intentions.

> **2. Response to your concern about "The test set is too small"**

As you pointed out, we have retained 50 instances for each language in the test set (aka the pilotset in our paper). The specific details are as follows:

**「Involved Languages / Cultures」**

XNLI (15): ar, bg, de, el, en, es, fr, hi, ru, sw, th, tr, ur, vi, zh

XQuAD (12): ar, de, el, en, es, hi, ro, ru, th, tr, vi, zh

XCOPA (11): en, et, ht, id, it, sw, ta, th, tr, vi, zh

GlobalOpinionQA (24 cultures): am, ar, bn, de, el, en, es, fr, hi, id, it, ja, ko, nl, pt, ru, sv, sw, tl, tr, uk, ur, vi, zh-CN (QA pairs are all presented in English)

**「Sample Size (50 samples per language / culture)」**

XNLI: $50 \times 15 = 750$

XQuAD: $50 \times 12 = 600$

XCOPA: $50 \times 11 = 550$

GlobalOpinionQA: $50 \times 24 = 1200$

And for multilingual tasks (XNLI, XQuAD, XCOPA), we ensure that each language set consists of the same 50 questions, presented in different language versions.

The reason we have sampled only 50 instances per language is due to the extensive scale of our experiments. As noted between "lines 213–215" of our manuscript, to obtain the instance-aware upper bound of XTransplant, we perform inference on all N^2 possible source and target layer selection strategies for each instance. For example, in LLaMA-2-7B-Chat with N=32, N^2=1024 times inferences are conducted per instance. Our main experiments involve 3 LLMs and 4 pilotset datasets, resulting in over 800 hours of computation on 8 * A800-SXM4-80GB.

We appreciate you for pointing this out and hope you could understand the computational constraints.
Reply to your comments (part 1)
Yes
No ethics review needed.
- What is the reason for applying XTransplant only when generating the first new token?
- In an ideal situation, XTransplant can enhance task-solving performance by accessing the knowledge of centric languages like English.
- In an ideal situation, XTransplant can work on both English- and Chinese-centric LLMs.
- This work investigates all possible pairs of source and target layers.
In this paper, the authors propose a new method, XTransplant, to exploit English-based capabilities within non-English contexts using English-centric Large Language Models (LLMs). For this purpose, XTransplant utilizes the feed-forward activations of a decoder layer on English text into a layer of the decoder on non-English input (or from non-English into English) while predicting the first new token. To select optimal pairs of layers in different languages, the authors investigate all possible combinations and their performance, referred to as the instance-aware upper bound in this paper. The experimental results on XNLI, XQuAD, and XCOPA with LLaMA-2-7B-Chat, Mistral-7B-Instruct-v0.3, Qwen2-7B-Instruct, and Chinese-Alpaca-2-7B show that the proposed XTransplant improve the performance under the setting of the instance-aware upper bound.
- In the experiments, the authors compared the upper bound results of XTransplant using the best combination of layers with baseline results. This is unfair and should not be reported as the main result of XTransplant. Reporting the result for analyses is acceptable. Instead, the authors can use the average or median performance in Figure 5 as the main result.
- For fair comparison, the authors need to decide the pair of source and target layers based on the performance of validation data.
- Furthermore, the test set size for each language is limited to 50 instances. This size is quite small. The authors need to consider the variance of the results for each language in such a small setting. Thus, increasing the test set size is required to make the results more reliable.
- When targeting cross-lingual tasks, utilizing machine translation is one of the easiest way. However, such a basic approach is not considered as a baseline in the paper. Instead, the authors concatenate the two different languages in PIM, a baseline approach.
- Considering the computational inefficiency of choosing the upper bound pairs of layers, reporting the computational cost of doing that is also required.
Yes
No ethics review needed.
- Why do you only use En->non-En configuration in cross-lingual tasks and non-En->En in cross-cultural tasks? 

- Did you observe 0.0 accuracy in the English subset of XCOPA for Qwen-2? This score seems dubious in light of the model’s technical report.
- The authors present an in-depth discussion of the proposed methods, analyzing the statistics across different tasks and providing examples of cross-cultural prompts to demonstrate how the method works on a low level. 


- The scope of experiments is broad and encompasses multiple tasks and models, including an instance of a language model trained on Chinese as a majority language.


- The method achieves significant improvements over the baselines, which is impressive for such a not complicated approach. However, this may be due to the reasons described in the weaknesses.
The article focuses on the performance of language models in distinct languages and cultural contexts. Past findings show that popular LMs perform much better in English than in other languages, which is caused mainly by the unequal composition of their training data. 
This work considers a trade-off between two options for applying LMs to distinct languages: 1) 
prompt models in English, to use its strong performance in the high-resource language or 2) prompt in the target language, potentially unlocking culture/language-specific information obtained by the model.

To leverage the promises of the two mentioned approaches, the authors propose a new method (UpperBound XTransplant) that patches the latent embedding to improve model performance in multilingual prompt-based tasks. The proposed algorithm finds the pair of layers between which the representation is transferred: from the prompted model to the target language prompted model. A greedy search is performed across N^2 possible pairs (where N is the number of layers) with the objective of finding the pair of layers for which transfer would maximize the probability of predicting a gold answer token.
This simple method offers a significant improvement in solving cross-lingual and cross-cultural tasks but also poses a risk of lurking into the gold answer when modifying the latent representation of the model.
- My main criticism is based on my strong suspicion that the improvements of the UpperBound method result mainly from lurking into the gold answer, i.e., the test set is used for tuning the methods. One indicator of that is the large improvement in English2English setting, which should not benefit at all from the enhanced cross-lingual transfer. This questionable result puts the whole point of improving multilingual capabilities in doubt. One solution to resolve that would be testing UpperBound with constant layer pairs for each language, e.g., after determining them based on a devset.

- Greedy search comes with a high computational cost of performing O(N^2) additional predictions. The authors mention that this could be alleviated by always pathing from the last layer or to the target layer, this setting should be analyzed in more detail. Another option would be pre-setting layer pairs, as described in the previous point.

- Much less severe criticism is connected to the lack of results for base (i.e. not instructed) models. My guess is that they could be less influenced by the language of the prompt.
Yes
No ethics review needed.
1. When transplanting the feed-forward activations from non-English to English in the XQUAD task, is the model's output in English or the non-English language?

2. In Table 1, the performance on the English XNLI test set increases significantly from 60 to 94. Could you elaborate on the experimental settings or conditions that contribute to this substantial improvement?

3. The experimental setup regarding the selection of the i-th layer and j-th layer for MSi→Tj (x) in Table 1 and Figure 5 is not clearly explained. Could you provide more details on how the layers are chosen and how the transplantation is performed?
The paper introduces the X Transplant method, a novel way to enhance the multilingual capabilities of large language models by cross-lingual transplantation of feed-forward activations.

Extensive experiments demonstrate improvements in both multilingual capabilities and cultural adaptability.
The paper introduces "XTransplant," a method designed to enhance the multilingual capabilities and cultural adaptability of LLMs by cross-lingual transplantation of feed-forward activations. The paper highlights the unlocking of current LLMs' multilingual potential and suggests promising directions for future research.
1.  Lack of comparative comparisons: The paper lacks comparisons with task-specific supervised fine-tuning and translation-then-inference approaches (translating prompts with a translation tool and then performing inference with the translated prompts). These methods represent alternative upper bounds for multilingual LLMs. Additionally, there are no comparisons between LLMs of different model sizes, which could provide insights into the impact of model capacity on performance.

2. Why not patching hidden states: While the method involves transplanting feed-forward activations from non-English inputs to English, it does not explore the potential of directly patching the hidden states of English into the processing of non-English inputs. 

3. Implications for improving multilingual LLMs: How can the conclusions of this work be utilized to enhance the performance of multilingual LLMs or inform continued training strategies? Providing deeper insights into the practical applications of your findings would increase the importance of the research.

4. Limited practical applicability: The approach requires parallel bilingual inputs (non-English and corresponding English sentences). In real-world applications, such parallel data may not be readily available, limiting the practical applicability of the method.
Yes
No ethics review needed.
$\textbf{Suggestions / Questions}$:


- line 52-53: "curse of multilinguality" and "negative interference" are basically talking about the same thing (or more correctly, the curse of multilinguility if one type of negative interference when the languages are so many), the authors could consider condensing the two sentences.

- Figure 1 is not clear how the method is carried on. the authors could cosider have one sentence in the caption to describe how the proposed method works.

- line 74, "given a certain question", the authors should limit the span of such a question, i.e., a question that requires knowledge learned in non-English texts

- line 145: what is the "another version" of x_s, I would assume it is the translation. the author should specify it.

- line 161: what is the intuition of only changing the first new token? If the first new token does not change before and after the transplanting, the remaining tokens will be the same (greedy decoding).

- section 3.2: I am not sure I understand why it is called bi-directional transplant. It is either En -> non-EN or non-EN -> EN for a specific prompt. I would be more inclined to call it Mutual or Parallel transplant.

- section 5.1: how do the authors define language consistency? I guess it is the frequency of the input and output being in the same language. The authors should make it clear. Additionally, my intuition is that Xtransplant should be bad for language consistency because it changes the original activation to the activations obtained from another language. However, the authors' results suggest this is not the case. Can the authors give explanations? Additionally, it would be interesting to see some actual examples.

- line 203-204: "Questions in above datasets are in different multilingual languages". I don't understand this sentence but I guess the author means "Each question in the above datasets is available in multiple languages"?


$\textbf{typo}$:

Line205: "we we performed" -> "we performed"
- The paper is well-motivated. I think the idea is interesting and novel.
- The experiments are extensive.
- The results reported in the paper show that XTransplant can consistently improve the performance.
This paper proposed a method called XTransplant to benchmark the upper bound performance in two scenarios: multilingual capability and cultural adaptability. Specifically, XTransplant replaces the feed-forward activations from one source language input to the target language input in the inference. For multilingual capability, the direction is En -> non-En, aiming to leverage good English generation capability. For cultural adaptability, the direction is non-En -> En, aiming to leverage the knowledge potentially only encoded in the non-En language-specific neurons.
- I understand that the main motivation is to benchmark the upper bound. But I see it is super expensive to investigate all the combinations (NxN). That might be a problem if one wants to see the performance on a specific downstream task with limited computation resources. 

- I also have concerns about whether xTransplant really offers the upper bound of a model, without any theoretical proof. One could argue it is also possible to transplant self-attention outputs.
@inproceedings{
anonymous2024xtransplant,
title={{XT}ransplant: A Probe into the Upper Bound Performance of Multilingual Capability in {LLM}s via Cross-lingual Transplantation},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=r3GxWNGpSj},
note={under review}
}
Current large language models (LLMs) often display significant imbalances in their multilingual capabilities and cultural adaptability, primarily due to their unbalanced and English-centric pretraining data.
For these English-centric LLMs, the disparities between English and non-English languages hinder their ability to utilize their robust English-based capabilities within non-English contexts, while also limiting access to valuable multilingual knowledge derived from non-English "language-specific neurons" within English contexts.
Motivated by this, our work explores the possibility for LLMs to leverage the strengths of both English and non-English languages, aiming to further unlock their multilingual potential.
To this end, we propose a probing method named $\mathcal{X}$Transplant, which directly transplants feed-forward activations from English input to non-English (or from non-English to English) during inference stage, allowing the model to benefit from both English and additional multilingual knowledge.
Through extensive experiments on our pilotsets and representative LLMs across different tasks and languages, we empirically prove that both the multilingual capabilities and cultural adaptability of LLMs hold the potential to be significantly improved by the cross-lingual feed forward transplantation, respectively from $\texttt{En} \rightarrow \texttt{non-En}$ and $\texttt{non-En} \rightarrow \texttt{En}$. 
Additionally, we also establish the upper bound performance of LLMs obtained through $\mathcal{X}$Transplant (relative growth of +80\% in multilingual capabilities, +39\% in cultural adaptability), highlighting the underutilization of current LLMs' multilingual potential. 
We do hope our further analysis and discussion could suggest promising directions for deeply unlocking the multilingual potential of current English-centric LLMs.
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Large language models Multilingual capability Feed forward activations Upper Bound Performance
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XTransplant: A Probe into the Upper Bound Performance of Multilingual Capability in LLMs via Cross-lingual Transplantation
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