SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings
Kang Liu
TL;DR
SetCSE introduces a novel framework that uses set operations and contrastive learning to improve sentence embeddings for complex information retrieval tasks involving intricate semantics.
Contribution
It proposes a new set-based framework with contrastive learning and set operations to enhance sentence embeddings for structured semantic querying.
Findings
SetCSE improves the discriminatory power of sentence embeddings.
It enables complex retrieval tasks with convoluted prompts.
The framework aligns well with human language semantics.
Abstract
Taking inspiration from Set Theory, we introduce SetCSE, an innovative information retrieval framework. SetCSE employs sets to represent complex semantics and incorporates well-defined operations for structured information querying under the provided context. Within this framework, we introduce an inter-set contrastive learning objective to enhance comprehension of sentence embedding models concerning the given semantics. Furthermore, we present a suite of operations, including SetCSE intersection, difference, and operation series, that leverage sentence embeddings of the enhanced model for complex sentence retrieval tasks. Throughout this paper, we demonstrate that SetCSE adheres to the conventions of human language expressions regarding compounded semantics, provides a significant enhancement in the discriminatory capability of underlying sentence embedding models, and enables…
Peer Reviews
Decision·ICLR 2024 poster
1. Provides a well-defined and practical framework that enables complex information retrieval tasks which are not possible with current search methodologies. 2. Applies contrastive learning to sentence embeddings in a unique way, emphasizing contextual differentiation between sets of sentences. 3. Offers compelling real-world applications, such as parsing nuanced topics like ESG stances from earnings calls, highlighting the framework’s potential for practical deployment.
1. There is no mention of an error analysis which would be beneficial in understanding the limitations of SetCSE in certain scenarios. 2. The applications of complex semantic search, data annotation, and new topic discovery are very cool with the detailed examples, but there is not quantification here or comparison with others with existing set methods from the literature (same with Table 1 and 2 as well). Do you have comparisons with other methods from the literature on this topic? Typos: Sec
The idea of this paper is really clever, simple and straightforward. The motivation is there, provide a LLM with notions of set theory to improve it in terms of search capabilities. Experimentation seems reasonable and enough. Proves the point authors want to provid
The authors make a good point to show the capabilities brought by the new training regime. However, there is no analysis on capabilities that are lost because of it. Do the models train with this regime underperform on sentence similarity or information retrieval datasets.
1. The paper formulates the sentence retrieval problem as a combination of sentence-set similarity and set operations, which is novel for the community. 2. The paper provides a comprehensive set of experiments, introducing two new settings: set intersection and set differences. It uses multiple baselines to demonstrate the robustness of the framework. The paper also offers sentence embedding visualizations to illustrate the improvement in sentence representations. Additionally, the paper presen
1. The paper would benefit from an additional experiment on sentence retrieval, similar to the one in Section 6.1. In this setting, the paper could compare its performance against traditional retrieval-based methods such as DPR and BM25 to better illustrate the model's improvements. Furthermore, in Table 2, certain baseline models do not show significant improvements with SetCSE. For instance, the improvements for SGPT are marginal when compared to other baselines. The paper should include an an
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multi-Agent Systems and Negotiation
MethodsSparse Evolutionary Training · Contrastive Learning