Papers with information retrieval tasks
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models (2023.acl-long)
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| Challenge: | Existing methods for retrieval-oriented language models focus on contextualized embedding of the [CLS] token, but recent study shows that ordinary tokens besides [CLL] may provide extra information, which help to produce a better representation effect. |
| Approach: | They propose a method where all contextualized embeddings of pre-trained model can be jointly pre-trained for retrieval tasks. |
| Outcome: | The proposed method improves the quality of representation where all contextualized embeddings of the pre-trained model can be leveraged. |
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models (2025.findings-emnlp)
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| Challenge: | a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods is presented. |
| Approach: | They evaluate 22 reranking methods including 40 variants across established benchmarks . primary goal is to determine whether performance disparity exists between LLM-based reranters and lightweight counterparts based on novel queries . |
| Outcome: | The proposed methods perform better on familiar queries than lightweight models, the authors show . |
Explaining Text Similarity in Transformer Models (2024.naacl-long)
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| Challenge: | Modern foundation models provide flexible text representations that enable the detection of semantic structure in vast amounts of unlabeled data. |
| Approach: | They propose to leverage layer-wise relevance propagation to understand the inner prediction mechanisms of NLP models by analyzing grammatical interactions, multilingual semantics, and biomedical text retrieval. |
| Outcome: | The proposed methods demonstrate their utility in three corpus-level use cases, analyzing grammatical interactions, multilingual semantics, and biomedical text retrieval. |
SDR: Efficient Neural Re-ranking using Succinct Document Representation (2022.acl-long)
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| Challenge: | BERT based ranking models have been successful on various information retrieval tasks, but they are prone to storage and network fetching latency. |
| Approach: | They propose a late-interaction architecture that allows pre-computation of intermediate document representations, thus reducing latency. |
| Outcome: | The proposed model achieves 4x–11.6x higher compression rates on the MSMARCO passage re-reranking task compared to existing methods. |
xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering (2021.acl-long)
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| Challenge: | Existing approaches to find relevant passages using sparse keywords are not effective for open domain question answering. |
| Approach: | They propose a new contrastive learning method for learning a dual-encoder model for question-passage matching using a large pool of negative samples. |
| Outcome: | The proposed method maintains large pool of negative samples and optimizes question-to-passage and passage-to question matching tasks. |
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers (2023.emnlp-main)
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Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Sultan, Christopher Potts
| Challenge: | Existing methods for information retrieval tasks require large labeled datasets for fine-tuning, but they can experience significant drops in accuracy due to distribution shifts from the training to the target domain. |
| Approach: | They propose a method for using large language models to generate large numbers of synthetic queries cheaply using an expensive LLM. |
| Outcome: | The proposed method boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
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| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
Poisoning Retrieval Corpora by Injecting Adversarial Passages (2023.emnlp-main)
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| Challenge: | Dense retrievers have outperformed traditional lexical methods in a range of information retrieval tasks, but to what extent can they be safely deployed in real-world applications? |
| Approach: | They propose a method where a malicious user injects a small number of adversarial passages into a retrieval corpus to maximize similarity with a set of training queries. |
| Outcome: | The proposed attack fools retrieval systems into returning top results for queries not seen by the attacker. |
DocSplit: Simple Contrastive Pretraining for Large Document Embeddings (2023.findings-emnlp)
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| Challenge: | Existing model pretraining methods only consider local information, resulting in low-quality embeddings for large documents. |
| Approach: | They propose a new method which forces models to consider the entire global context of a large document. |
| Outcome: | The proposed method outperforms existing models on document classification, few shot learning, and retrieval tasks. |
The Ranking Blind Spot: Decision Hijacking in LLM-based Text Ranking (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance in information retrieval tasks like passage ranking. |
| Approach: | They propose two attacks that aim to force the LLM ranker to prefer a specific passage and rank it at the top. |
| Outcome: | The proposed attacks aim to force the LLM ranker to prefer a specific passage and rank it at the top. |