| Challenge: | Recent work on generative ranking models for Information Retrieval has focused on discriminative methods that learn a similarity function to compare questions and candidates answers. |
| Approach: | They propose to use a language model to train a ranking function that model the semantic similarity of documents and queries instead of discriminative ranking functions. |
| Outcome: | The proposed approaches are as effective as state-of-the-art discriminative models for the answer selection task and show unlikelihood losses are reduced for IR. |
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| Challenge: | Existing IR techniques contain deficiencies, posing a performance bottleneck . combining diverse approaches to retrieve information is a viable strategy . |
| Approach: | They propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. |
| Outcome: | The proposed method outperforms existing methods on two Retrieval Question Answering tasks. |
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
| Challenge: | Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking. |
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Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance (2025.findings-emnlp)
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| Challenge: | a new approach to training with binary relevance labels uses synthetic data . contrastive learning with binary correlations leaves out subtle nuances useful for ranking . |
| Approach: | They propose to use waterstein distance as a loss function for training transformer-based retrievers with graduated relevance labels instead of real documents. |
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Neural Retriever and Go Beyond: A Thesis Proposal (2022.naacl-srw)
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| Challenge: | Existing neural retrievers are developed for pure-text queries, which prevents them from handling multi-modality queries. |
| Approach: | They propose methods to address issues of existing neural retrievers from three angles . they propose new model architectures, IR-oriented pretraining tasks and generating large scale training data . |
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Embedding Meta-Textual Information for Improved Learning to Rank (2020.coling-main)
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| Challenge: | a neural representation learning approach has not been extended to meta-textual information that is readily available for many IR tasks. |
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Modularized Transfomer-based Ranking Framework (2020.emnlp-main)
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| Challenge: | Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. |
| Approach: | They propose to modularize a Transformer ranker into separate modules for text representation and interaction. |
| Outcome: | The proposed model is faster than previous models and is easier to interpret and understand. |
Exploring efficient zero-shot synthetic dataset generation for Information Retrieval (2024.findings-eacl)
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| Challenge: | Recent advances in large language models offer a new avenue of generating synthetic training data to train neural retrieval models for unlabelled data collections. |
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DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)
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| Challenge: | Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids . |
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InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)
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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li
| Challenge: | Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation. |
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| Outcome: | The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms. |
Re3val: Reinforced and Reranked Generative Retrieval (2024.findings-eacl)
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| Challenge: | generative retrieval models encode pointers to information in a corpus as an index within the model’s parameters. |
| Approach: | They propose a generative retrieval model that leverages contextual information to rerank retrieved page titles and utilizes REINFORCE to maximize rewards generated by constrained decoding. |
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