Title: EAReranker: Efficient Embedding Adequacy Assessment for Retrieval Augmented Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Retrieval-Augmented Generation

Embedding-based Reranking

Document Adequacy Assessment
Abstract: With the increasing adoption of Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, ensuring the adequacy of retrieved documents has become critically important for generation quality. Traditional reranking approaches face three significant challenges: substantial computational overhead that scales with document length, dependency on plain text that limits application in sensitive scenarios, and insufficient assessment of document value beyond simple relevance metrics.  We propose EAReranker, an efficient embedding-based adequacy assessment framework that evaluates document utility for RAG systems without requiring access to original text content. The framework quantifies document adequacy through a comprehensive scoring methodology considering verifiability, coverage, completeness and structural aspects, providing interpretable adequacy classifications for downstream applications. EAReranker employs a Decoder-Only Transformer architecture that introduces embedding dimension expansion method and bin-aware weighted loss, designed specifically to predict adequacy directly from embedding vectors. Our comprehensive evaluation across four public benchmarks demonstrates that EAReranker achieves competitive performance with state-of-the-art plaintext rerankers while maintaining constant memory usage ($\sim$550MB) regardless of input length and processing 2-3x faster than traditional approaches. The semantic bin adequacy prediction accuracy of 92.85\% LACC@10 and 86.12\% LACC@25 demonstrates its capability to effectively filter out inadequate documents that could potentially mislead or adversely impact RAG system performance, thereby ensuring only high-utility information serves as generation context. These results establish EAReranker as an efficient and practical solution for enhancing RAG system performance through improved context selection while addressing the computational and privacy challenges of existing methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\n## Summary\n\nThe paper introduces EAReranker, an efficient embedding-based framework for assessing document adequacy in RAG. EAReranker aims to address challenges of traditional reranking approaches by evaluating document utility without accessing original text content. It uses a decoder-only architecture for predicting adequacy directly from embedding vectors. An experiment on four benchmarks shows competitive performance with existing rerankers.\n\nSTRENGTHS AND WEAKNESSES:\n## Strength\n\nS1. Adequacy is an overlooked and timely research area in the applications of RAGs. I appreciate that the paper focuses on this important topic.\n\nS2. The proposed EAReranker method is sound. The proposed framework to assess adequacy looks useful to the community. The experiments are thorough, and the findings are promising.\n\nS3. The paper is well-written and easy to follow.\n\n## Major Weakness\n\nW1. The framework for assessing adequacy could be strengthened with more detailed explanations. For instance, the rationale behind the four dimensions of adequacy assessment needs clarification. Are these dimensions based on prior research? What is the reasoning for choosing six distinct bins with varying ranges?\n\nW2. The challenges of existing approaches are (1) computational overhead, (2) dependency on plain text, (3) lack of refined content value assessment. The proposed method only addresses (1) and (3). Does EAReranker support non-text data? Additionally, if users must train their own EAReranker, this could reduce efficiency compared to methods that don't require training.\n\n## Minor Weakness\n\nM1. The text in figure 1 is too small to see.\n\nM2. RAG and embedding search work on multimodal content as well. (L39)\n\nQUESTIONS:\n1. How is the framework for assessing adequacy developed? Are these dimensions based on prior research? What is the reasoning for choosing six distinct bins with varying ranges?\n2. Does EAReranker support non-text data?\n3. Are users expected to train their own EAReranker? Or the trained EAReranker will be available for users?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper points to the issues of high computational overheads, dependency on plain text and insufficient assessment of document value in current RAG systems. To address these issues the authors propose to sue Decoder only architectures which takes embedding vectors as input where the embedding vectors are encodings of the input as given by a retrained embedding vector. The paper also claims to have constructed a new adequacy assessment metric.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The paper considers an important problem\n2. New dataset is curated\n\n**Weaknesses**\n1. The paper keeps on talking about the high level ideas abut does not provide a concrete methodology to address the discussed issues. As an example see Section 31. which I again repetition of what was said in the Introduction but it is not clear how the goals are actually achieved. I also do not see any mathematical description of the adequacy metric are at an extremely high level. Algorithm 1 which seems to be important in calculating some metric is not even explained in the text.\n2. One of the issues considered is privacy. I do not see how just using pertained encoders to separately extract embeddings solve the issue. What is the threat model there? What if the attacker also has access to the pre-trained encoder? Why using pertained encoder with plain text is safer than the whole system?\n3. I do not see much contribution of the work except a new dataset. Using embeddings instead of plain text is straightforward. it is not even directly clear how it solves the issues of computational overhead and privacy.\n\nQUESTIONS:\nIna addition to my questions posed in the weaknesses please explain exactly how your proposed architecture mitigates issues of computational overheads and privacy preservation?\n\nDescribe the adequacy metric mathematically. How does each component relate to and assure the four requirements specified in Section 4.1?\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a methodology for ranking measuring both the relevance and adequacy of documents to be input to a reader in a RAG pipeline. They propose an annotation process and a predictor for this adequacy often finding that their approach can be more efficient while maintaining effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The problem addressed is important as cutoff values presented in works become increasingly shallow.\n- Writing is clear.\n- Several benchmarks are utilized across multiple domains.\n\nWeaknesses\n- The authors should clarify their definition of adequacy, the notion of a comprehensive set of information would appear to better relate to recall. The use of accuracy doesnt appear to assess the criteria from this approach is motivated.\n- The methodology in dataset curation appears to apply a point-wise scoring method for how comprehensive each text is as opposed to each ranking, this feels largely identical to the criteria used in relevance judgements (See Umbrella[1] for automated judgements and DL-19 [2] for human criteria).\n- The authors note a multifaceted evaluation process however it is ambiguous how effectiveness is further decomposed beyond accuracy-based measures.\n- The work is effectively an adapter and thus should be compared, see preTTR [3] or more generally model adapters [4, 5]\n\nReferences:\n\n[1]: Upadhyay, S., Pradeep, R., Thakur, N., Craswell, N. and Lin, J., 2024. \nUmbrela: Umbrela is the (open-source reproduction of the) bing relevance\n assessor. *arXiv preprint arXiv:2406.06519*.\n\n[2]: Craswell, N., Mitra, B., Yilmaz, E., Campos, D. and Voorhees, E.M., 2020. Overview of the TREC 2019 deep learning track. *arXiv preprint arXiv:2003.07820*.\n\n[3]: MacAvaney, S., Nardini, F.M., Perego, R., Tonellotto, N., Goharian, N. \nand Frieder, O., 2020, July. Efficient document re-ranking for \ntransformers by precomputing term representations. In *Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval* (pp. 49-58).\n\n[4]: Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M. & Gelly, S.. (2019). Parameter-Efficient Transfer Learning for NLP. <i>Proceedings of the 36th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i> 97:2790-2799 Available from https://proceedings.mlr.press/v97/houlsby19a.html.\n\n[5]: Zhang, R., Han, J., Liu, C., Gao, P., Zhou, A., Hu, X., Yan, S., Lu, P.,\n Li, H. and Qiao, Y., 2023. Llama-adapter: Efficient fine-tuning of \nlanguage models with zero-init attention. *arXiv preprint arXiv:2303.16199*.\n\nQUESTIONS:\nThe pipeline relies entirely on LLM agreement; no human annotation is described. Was any manual spot-checking performed to confirm bin labels, especially for boundary cases?\n\nCould the authors rerun key experiments with multiple seeds and provide 95 % confidence intervals or paired tests against baselines?\n\nAlgorithm 1 accepts a subset when all four scores lie within 0.2 of the mean. Why was 0.2 chosen, and how robust is the dataset size/quality if this tolerance is tightened or relaxed?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel embedding-based re-ranking method (adequacy as the score to re-rank), EAReranker, to address three challenges that traditional re-ranking approaches face: computational overhead scaling with document length, dependency on plain text, and assessment of document value beyond relevance metrics. EAReranker first expands the embeddings from an existing embedding model (such as bge-m3) using multiple independent projection layers, and then trains L stacked Transformer Decoder layers with the adequacy scores obtained from multiple LLMs through their adequacy assessment annotation framework. The effectiveness of EAReranker is demonstrated through examining its ranking performance (on 2 English retrieval datasets from BEIR and 2 Chinese retrieval datasets from C-MTEB) and its adequacy assessment accuracy (on the curated datasets based on bge-m3-data).\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n- The proposed method is novel and inspiring. The three challenges that EAReranker aims to address are practical in real-world scenarios.\n- Evaluation results on 4 retrieval datasets are convincing. EAReranker's ranking performance is competitive with those of superior plain-text re-rankers.\n- The paper is well-written, and the structure of the paper is clear. The figures presented in the paper are also helpful for clearly understanding the proposed method. \n\n# Weaknesses \n- The experimental settings on in-domain / out-of-domain are not presented in detail. I'm worried about the generalizability of EAReranker. Many existing works [1,2] usually use MSMARCO [3] as the training dataset and use the other 14 datasets in BEIR [4] as the evaluation datasets. I'm curious about the performance of EAReranker under this setting. **Another option** is using the well-trained EAReranker to perform evaluation on some other OOD datasets, such as AIR-Bench [5].\n- One main challenge that EAReranker aims to address is the computational overhead scaling with document length. The 4 retrieval datasets used in the experiment all have relatively short documents in their corpora. MLDR [6] and NarrativeQA [7] are two long-document retrieval datasets used in the experiments of BGE-M3 [8]. Experimental results on these datasets would increase the soundness of the paper.\n- The ablation study of the \"within-bin score calibration methodology\" introduced in Section 4.3 is missing.\n\n[1] RepLLaMA: https://arxiv.org/pdf/2310.08319 \\\n[2] Llama2Vec: https://aclanthology.org/2024.acl-long.191.pdf \\\n[3] MSMARCO: https://arxiv.org/pdf/1611.09268 \\\n[4] BEIR: https://arxiv.org/pdf/2104.08663 \\\n[5] AIR-Bench: https://arxiv.org/pdf/2412.13102 \\\n[6] MLDR: https://huggingface.co/datasets/Shitao/MLDR \\\n[7] NarrativeQA: https://arxiv.org/pdf/2310.19923 \\\n[8] BGE-M3: https://arxiv.org/pdf/2402.03216\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-06"}