Paper: LLM-Oriented Retrieval Tuner (ICLR-2024)

Reviewer comment (Experiments):
The same 6 BEIR datasets used for evaluation are also used in the alignment and uniformity analysis, which violates the zero-shot setting of BEIR and makes the comparison unfair with the other retrievers. The authors should at least evaluate on additional datasets that were not seen during the analysis. Otherwise, the gain from this alignment and uniformity analysis may have been attained from fitting to the test set.

Author response:
**1. About Zero-Shot Setting**

We respectfully disagree with your claims about violating zero-shot evaluation and fitting the test set. Our retrieval module neither uses any evaluation dataset for fine-tuning nor testing data to select model checkpoints. The alignment and uniformity analysis is also not to evaluate the retrieval performance of each LLM layer on the evaluation datasets.
Furthermore, we have supplemented the alignment and uniformity analysis for Train and Dev sets in these evaluation datasets (noting that not all datasets include both). Analysis results consistently show stable rankings for each LLM layer across Train, Dev, and Test sets. These results underscore that the alignment and uniformity analysis in our paper doesn't fit the test set.

You can find the analysis results through these anonymous links: [[GPT2-Large]](https://drive.google.com/file/d/1hr-AL-SqPSKEj9HkD4_9dTfXKL5l2tXe/view?usp=sharing), [[GPT2-XL]](https://drive.google.com/file/d/1cmAennw-yalv7zkJdG8uVEDA9V8mEHT_/view?usp=sharing), [[GPT-j-6B]](https://drive.google.com/file/d/1g0KLqvJZOB9orXvrVXNRdHDwRr5BVUnS/view?usp=sharing).


**1. About Zero-Shot Setting**

Thank you sincerely for your insightful suggestion. In response, we have augmented the alignment analysis results for the MS MARCO dataset. Considering the large scale of the complete MARCO dataset, our alignment analysis was conducted specifically on the first 5,000 training queries and their corresponding positive passages.

[[GPT2-Large-Marco-Alignment]](https://drive.google.com/file/d/1rPfhFKDX2L_-I8kmiDtcAIuLVpqKtQAg/view?usp=sharing)

[[GPT2-XL-Marco-Alignment]](https://drive.google.com/file/d/1Xmp0tZogFSXQWNzajmi7WxDnPe_2JTO7/view?usp=sharing)

[[GPT-j-6B-Marco-Alignment]](https://drive.google.com/file/d/19pm-vNdQOShMf5O_r22QebSoDHXMhvOa/view?usp=drive_link)

The analysis results above demonstrate that the optimal alignment layers on GPT-2-large, GPT-2-XL, and GPT-j-6B are consistent with the alignment layers selected in our paper. We hope these supplemental analysis results address your concerns about our zero-shot setups.

Reviewer comment (Experiments):
Table 2 compares only with LMORT variants that use a single layer (or two) of the LLM. Have you compared with additional baselines of fusing more layers of the LLMs (either via attention similar to that in LMORT, or via simpler pooling mechanisms)? Have you also tried layer selecting methods not based on alignment or uniformity? For instance, choosing first and last layer?

Author response:
Additionally, our current method design proved to be the most effective among the various solutions we  explored, including the options you mentioned such as simple pooling, fusing additional LLM layers, or using the first and last LLM layers directly.

Reviewer comment (Experiments):
Why do you only evaluate on 6 datasets from BEIR out of 18? It is unclear to me why the rest of BEIR is omitted and makes the results less convincing.

Author response:
The paper has explained the reasons for using the six BEIR datasets for evaluation (P4)--conducting inference on a larger corpus with LLMs surpassed our current computational and storage resources.

Reviewer comment (Theory):
The method relies heavily on the frozen LLM, which might constrain the adaptability of the LMORT in diverse real-world retrieval scenarios where the underlying pretrained model isn't perfectly aligned with the task at hand.

Author response:
Our retrieval module is specifically tailored for a frozen LLM, denoting an LLM that has not undergone fine-tuning for a DR task. Theoretically, our method can be applied to LLMs that have been fine-tuned for various text processing and generation tasks. We are conducting experiments on [LLAMA2-7B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), an LLM fine-tuned on diverse instruction datasets. Preliminary results suggest that this LLM displays a consistent mutually exclusive phenomenon of uniformity and alignment across different layers, in line with our paper's findings. You can find the analysis results in [[this anonymous link]](https://drive.google.com/file/d/1Qel8mllDdV-GDolHvJfQLVHkWjVpZcY6/view?usp=sharing).

Reviewer comment (Theory):
The paper doesn't seem to discuss or account for potential edge cases where the bifurcation between alignment and uniformity layers might cause misrepresentations or omissions of critical data.

Author response:
We would sincerely appreciate it if you could provide additional details or further elaborate on the edge cases mentioned in "Weakness 3".

Reviewer comment (Theory):
Within the objective function, there's a pronounced use of the negative logarithm operating on an exponential function related to similarity measures. This raises concerns:
\[ 
\theta^* = \text{argmin}_{\theta} \sum_{X_q \in Q} \sum_{X^+ \in C^+_{X_q}} -\log \left( \frac{ \exp(\text{sim}(X_q, X^+; \theta, \tilde{\phi}_{llm}))}{\exp(\text{sim}(X_q, X^+; \theta, \tilde{\phi}_{llm})) + \sum_{X^-_p \in C^-_{X_q}} \exp(\text{sim}(X_q, X^-_p; \theta, \tilde{\phi}_{llm}))} \right)
\]  
Could this cause potential issues in terms of stability or convergence, especially with extremely high or low similarity scores?

Author response:
We adopt a widely-used cross-entropy loss in DR model training, such as [GTR](https://aclanthology.org/2022.emnlp-main.669.pdf) and [CPT](https://arxiv.org/pdf/2201.10005.pdf), without introducing any modifications. We would like to provide a gentle reminder that the concerns you've raised regarding this loss function appear to extend beyond the scope of our current work. However, in light of your concerns, we have examined the curves of the model training losses. Our experimental results suggest that the stability and convergence of the training objective is robust. You can find the training curves in [[this anonymous link]](https://drive.google.com/file/d/1FWOuEQrG9cvJWuAYP0Q21QJvmir7-0bA/view?usp=drive_link).

Reviewer comment (Experiments):
The paper may further explore the capability of the proposed method when using other (larger) LLMs, such as Llama 2 and Falcon (or Flan-T5). The behavior of different LLMs may vary. Although the paper claims general contributions to 'LLM-Oriented Retrieval Tuner', limited LLMs are used in the evaluation.

Author response:
We are currently conducting experiments with LLAMA2-7B and have observed a consistent manifestation of layered exclusion in alignment and uniformity. These findings align with our previous observations in the GPT series LLM. Please see [[this anonymous link]](https://drive.google.com/file/d/1K4A-8FYlyQh7k_CAVbNZETEhJrbfzLdT/view?usp=sharing) for the result.

Reviewer comment (Reproducibility):
The designed approach seems to rely on the assumption that the LLM models can be accessed. Currently, there are very powerful LLMs which only provide API access, where the proposed method cannot be directly applied.

Author response:
In the case of closed-source LLMs, our method is applicable if their API provides access to the hidden states of the optimal alignment layer and the uniform layer, eliminating the need to open-source the parameters of the LLM. 
Additionally, we would like to gently convey the perspective that the worth of research should not be exclusively defined by closed-source APIs.

Reviewer comment (Theory):
**Unclear motivations and lack of the connections between the motivations and the proposed method design**: I understand that alignment matters in text retrieval but why does uniformity matter? While the authors cited Wang and Isola (2020) briefly, papers should be self-containing and as the cited prior work focuses on non-text domains, more careful consideration and discussions on whether this assumption holds in the text retrieval task should be discussed more. If the final goal based on this motivation is to make the two properties distributed across different layers, why adding the proposed layers can be an effective solution? Are there any alternative solutions? For instance, several works introduce a new objective to align different layers in transformers. I think this proposed method could be one solution, but explanations on why this is the best way to address the discussed issue should be included.

Author response:
Thank you for your review. Our work's objectives may have some distinctions from your interpretation. We would appreciate it if you could review our explanation provided in the **General Response**.

**1. About Uniformity**

Many studies have demonstrated the significance of uniformity in text representation learning tasks, such as  [SimCSE](https://arxiv.org/pdf/2104.08821.pdf), [COCO-DR](https://arxiv.org/pdf/2210.15212.pdf), and [this work](https://openreview.net/pdf?id=MxvHVNukama).

Furthermore, a prototypical counterexample highlights the important role of uniformity in text representation learning. In a collapsed representation space where all text is projected to the same representation vector, alignment is good but uniformity is lacking, making such spaces unsuitable for retrieval.

Reviewer comment (Writing):
The phrase "larger LLMs" sounds strange, as LLMs themselves stand for "large language models." What is the boundary between larger large language models and smaller large language models?

Author response:
Thank you very much for your suggestions regarding some citation formats and terminology descriptions. We have already revised these parts.