RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation (2025.emnlp-main)
Copied to clipboard
Andrei Catalin Coman, Ionut Teodor Sorodoc, Leonardo F. R. Ribeiro, Bill Byrne, James Henderson, Adrià de Gispert
| Challenge: | Existing Reward Models (RMs) struggle in Retrieval Augmented Generation settings. |
| Approach: | They propose a method that repurposes question-answering datasets into preference pairs that prioritise groundedness over stylistic features. |
| Outcome: | The proposed method surpasses existing RMs trained on larger general corpora with an absolute improvement of +15.5%. |
Similar Papers
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)
Copied to clipboard
Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| Challenge: | Existing retrieval augmented language models often overlook effective alignment with human preferences. |
| Approach: | They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity . |
| Outcome: | The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to retrieve information from large language models (LLMs) but they fail to address the preference gap between retrievers and LLMs. |
| Approach: | They propose a retrieval module that dynamically injects retrieved information into the input context of large language models (LLMs) This approach aligns the retriever’s and LLM’s preferences by defining a new metric, “gain”, which measure how well an input passage contributes to correct outputs. |
| Outcome: | The proposed approach has shown significant success in various NLP tasks, but there is a preference gap between retrievers and LLMs. |
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to retrieval augmented generation (RAG) are based on parametric knowledge and external knowledge. |
| Approach: | They propose a weakly supervised method for training a relevance estimator (RE) that provides relative relevance between contexts as previous rerankers did, and provides confidence, which can be used to classify whether given context is useful for answering the given question. |
| Outcome: | The proposed framework improves previously unreferenced large language models and can be trained with a small generator without labels for correct contexts. |
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models struggle to evaluate the correctness of non-parametric knowledge when it differs from internal memorization, leading to knowledge conflicts during response generation. |
| Approach: | They propose a lightweight alignment method to leverage multi-source knowledge based on retrieval relevance. |
| Outcome: | Experiments on four datasets show that the proposed method outperforms RAG by 4-10% in accuracy without any extra component. |
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications. |
| Approach: | They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data. |
| Outcome: | Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields. |
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multiple fine-tuning strategies exist with different costs and benefits for RAG pipelines. |
| Approach: | They evaluate several RAG fine-tuning strategies with different costs and benefits . embedding and generator models can be fine- tuned to increase performance . |
| Outcome: | The proposed techniques improve quality metrics, but have different computational costs. |
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect. |
| Approach: | They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers. |
| Outcome: | The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers. |
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information. |
| Approach: | They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%. |