**For Reviewer iyxD**
I would like to express my gratitude for your detailed review and the valuable feedback provided.
>**Comment 1: Uncertain Novelty.**
We agree with you that introducing multiple negatives is important and has been widely explored in conventional recommenders, as we briefly discussed in section 3.2.
However, as your discovery, LM-based recommenders which has different training paradigm compared with traditional recommenders has just been explored, on which multiple-negatives has largely omitted and lack effectively method to introduce into training pipeline. To our knowledge, we are among the first to point out that multi-negatives is important in LM-based recommenders and propose to effectively instill partial ranking information into LM-based recommenders through an alternated softmax version of DPO.
>**Comment 2: Confusing Statement**
- Clarification of Equation 12:
Equation 12 is also an equivalent variant of the softmax loss, which can be rewritten as: $
E_{(u,i_p,I_d)}\left[{\rm log}\frac{{\rm exp}(f(u,i_p))}{{\rm exp}(f(u,i_p)) + \sum_{i_d \in \mathcal{I}_d}{\rm exp}(f(u,i_d))}\right] $
- We will modify line 225 to "indicating the significant roles of knowledge and reasoning ability in language models for recommendation tasks in semantically informative datasets."
- Untuned LM-based recommenders achieve suboptimal performance because of inadequate instruction-following capabilities (reflected by a low valid ratio and a low hit ratio) or lack of domain knowledge (reflected by a high valid ratio but a low hit ratio). ChatRec, with GPT-4 as its backend, falls into the second case. We will modify line 228 to avoid confusion.
> **Comment 3: Replicability.**
In fact, we have uploaded all the code and prompt used in the anonymous link " https://anonymous.4open.science/r/S-DPO-C8E0" which has been attached in the abstract of submitted manuscripts. We will further include all of our prompts in the appendix.
> **Comment 4: Evaluation.**
- We calculate further HitRatio and NDCG, please refer to supplementary material (Table 2) for results.
- Sorry for leading confusion, the results in Figure 2a is based on LLama2 instead of LLama, which will be modified in the manuscript.
> **Comment 5: Statistical Test.**
We utilize one-sided t-test for all of our statistical test and find the p value less than 0.05 among all shown experiments. Clarification will be included in implementation details.
> **Comment 6: Comparison.**
- It has been shown that 5 epochs is enough for model convergence. Follow your suggestions, we conduct SFT for further 3 epochs and results show that it bring no gain to the final performance.
- We are adding a dedicated hyperparameter table to provide transparency and demonstrate that each model was given an equal opportunity for optimization. Please refer to supplementary material (Table 1).
- **Regarding Figure 2b and 2c**:
We further ensure that the validation setting for DPO and S-DPO are consistent, allowing for a direct and fair comparison. The revised results are now included in the supplementary material (Figure 1). We compare loss, relative likelihood and absolute likelihood to validate the effectiveness of S-DPO
- **Regarding Figures 3b and 3c**:
A very low β overly prioritizes ranking information, compromising the model's ability to follow instructions, as seen in the decreased valid and hit ratios. Conversely, an excessively high β constrains the model too much by the reference model's standards, leading to lower performance (hit ratio@1). A slight increase in valid ratio with β values greater than 1 suggests better adherence to the reference model's constraints, supporting our interpretation.
>**Comment 7: Related work.**
We agree with your suggestion and we will reorganize our revised version. The lack of in-depth discussion is due to space constraints. We will include a more detailed discussion in the appendix.