reviews: 
Comments: The authors present a text classification framework based on measuring similarity between the input documents and multiple related and more specialized concepts that may be associated with label in the label set. To that end, based on my best understanding of the authors proposed method, a LLM is used to generate embedding vectors for each of the concept words (and associated definitions) and the input document. A cosine similarity is calculated between each concept embedding and input document and these similarity scores are then aggregated somehow and passed through an activation function to get final classification scores. There are also some learnable parameters in this setup but reading through the method description, it is unclear how they are actually used (somehow they are used to modify the concept embeddings, I think).

The method is compared to a standard classification fine-tuning setting where the model is trained to predict the classification label using an additional classification head on top of the base LLM. The evaluation is done on 4 sentiment analysis datasets. While the performance improvement is minimal on bert-based models (bert-base and roberta-large), the authors do observe 6-8% gain in absolute terms for t5-encoder.

The authors also perform analysis of what happens when different concept words (or definitions) are used at inference time. They find that definition paraphrasing or using different concept words but belonging to same domain do not impact performance significantly. They also add in some neutral terms to see how model react to those and found that sometimes model can still perform classification with accuracy even when they are not relevant.
Strengths: Originality: The idea of performing text classification by using more description concepts associated with text labels has significant merit. This is especially useful in cases where the labels in label set are vague or overbroad and training set may not cover all possible semantics that may be associated with the given label. As such, a method that allows us to inject more associated concepts at inference time without retraining to capture novel labels / label concept could have significant impact.
Weaknesses: The quality of writing is bad. An informed researcher in this area will have a hard time understanding both methodology and some of the experiments in the paper. In section 3 where authors describe their method, many of the symbols are undefined (for example, n_s, n_y, n_n, n_m, s_D, etc). Similarly, mathematical expressions are written without any context (and it is unclear if they are even meaningnful). For example, expression ∀i∃j • P(C(j), y(i)) has not meaning -- it has quantifiers but doesn't seem to be logical statement). Equations 1-5 that actually describe how embeddings are being used are un-parsable -- I am not sure what any of those expressions means (for example, each expression uses universal quantifier symbol but is not a logical statement). As such, I have no idea what exactly is the method that the authors are proposing (it might be useful to add some pseudocode or something similar in the appendix which might make things clearer).

Moving to evaluation section, in section 5.1, it is unclear how exactly neutral concepts injected (are concepts for positive or negative class replaced with neutral concepts or is it added as a separate class?). Overall, I don't think I can make an educated opinion about the experiment since they depend on understanding the method itself).
Recommendation: 1: strong reject
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 1 poor
Clarity: 1 poor