Papers with BEIR benchmark
ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated remarkable performance in document reranking tasks. |
| Approach: | They propose a two-stage training approach for document reranking using reinforcement learning and fine-grained score learning. |
| Outcome: | The proposed approach outperforms open-source and proprietary reranking models on BEIR benchmark. |
Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing approaches to enhance dense retrieval models are unwieldy, such as requiring explicit supervision, complex model architectures, or massive external models. |
| Approach: | They propose an unsupervised method to enhance passage retrieval in zero-shot settings by iterating a loop that a dense retriever learns from supervision signals provided by a reranker. |
| Outcome: | The proposed method outperforms leading supervised and unsupervised retrievers on the BEIR benchmark while showing strong adaptation abilities to tasks and domains that were unseen during training. |
Relevance-assisted Generation for Robust Zero-shot Retrieval (2023.emnlp-industry)
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| Challenge: | Despite strong in-domain performance, dense retrievers have shown poor generalization to out-of-domain zero-shot tasks where no training queries are available. |
| Approach: | They propose to generate domain-specific pseudo queries for fine-tuning with domain-relevant relevance between PQ and documents. |
| Outcome: | The proposed approach is more robust to domain shifts, validated on BEIR zero-shot tasks. |
Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories (2023.emnlp-main)
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| Challenge: | Using mixture-of-memory augmenting to augment language models improves model generalization but with diminishing return. |
| Approach: | They develop a mechanism that augments language models with mixture-of-memory Augmentation (MoMA) they augment strong T5-based retrievers with the option to "plug in" unseen memory at inference time. |
| Outcome: | The proposed model outperforms methods with larger model sizes on the BEIR benchmark and achieves comparable or even better performance than methods relying on target-specific pretraining. |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
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| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary (2023.acl-long)
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| Challenge: | Dense retrieval models based on text representations have proven very effective, but when applied off-the-shelf they often experience a severe drop in performance. |
| Approach: | They propose to interpret the vector representations produced by dual encoders by projecting them into the model’s vocabulary space. |
| Outcome: | The proposed model significantly improves on the BEIR benchmark and in zero-shot settings. |
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval (2022.findings-acl)
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| Challenge: | Experimental results show that LaPraDoR is state-of-the-art compared with supervised dense retrieval models. |
| Approach: | They propose a pretrained dual-tower dense retriever that does not require supervised data for training. |
| Outcome: | The proposed method achieves state-of-the-art performance on 18 datasets of 9 zero-shot text retrieval tasks. |
CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)
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| Challenge: | Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM. |
| Approach: | They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process. |
| Outcome: | Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages. |
PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking (2024.acl-long)
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| Challenge: | Existing methods for pairing ranking prompting only output the same label for comparison results of different confidence intervals without considering the uncertainty of pairwise comparison. |
| Approach: | They propose a pairwise ranking prompting approach that exploits the output probabilities of target labels to capture the degree of certainty of comparison results. |
| Outcome: | The proposed method shows strong robustness and acceptable efficiency on the BEIR benchmark. |
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
tRAG: Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025.naacl-long)
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| Challenge: | Neural retrieval models suffer when there is a domain shift between training and test data distributions. |
| Approach: | They propose to generate domain-adapted pseudo-queries using large language models (LLMs) to improve term recall of unseen query terms by using term-level Retrieval-Augmented Generation (tRAG). |
| Outcome: | The proposed method significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance. |
HIL: Hybrid Isotropy Learning for Zero-shot Performance in Dense retrieval (2024.naacl-long)
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| Challenge: | Recent advances in dense retrieval models have brought ColBERT to prominence in information retrieval, but it is underperforming in zero-shot tasks. |
| Approach: | They propose a Hybrid Isotropy Learning architecture that integrates isotropic and anisotropic representations to improve zero-shot retrieval performance. |
| Outcome: | The proposed model outperforms the baseline ColBERT model in BEIR benchmarks. |
FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
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| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
| Approach: | They propose a listwise LLM reranking approach that leverages the first generated identifier to obtain a ranked ordering of the candidates. |
| Outcome: | The proposed approach accelerates inference by 50% while maintaining robust ranking performance with gains across BEIR benchmark. |
DADA: Distribution-Aware Domain Adaptation of PLMs for Information Retrieval (2024.findings-acl)
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| Challenge: | Pre-trained language models struggle with out-of-domain data due to distribution shifts . generative domain adaptation (DA) methods have been proposed to address these issues . |
| Approach: | They propose a distribution-aware domain adaptation method to address distribution shifts in domains . they use observation-level feedback and observation- level feedback to adapt to the target domain . |
| Outcome: | The proposed method adapts to the domain distribution knowledge at the level of a single document and the corpus and expands document representation to unseen gold query terms using domain and observation feedback. |
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. |
| Approach: | They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge. |
| Outcome: | The proposed model outperforms a 3B supervised model on the BEIR benchmark. |
Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking (2025.emnlp-main)
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| Challenge: | Recent work has identified retrieval heads as a subset of attention heads responsible for retrieving salient information in long-context language models. |
| Approach: | They introduce a retrieval head that uses attention scores to enhance retrieval from long context . they use QRRetriever to select the most relevant parts with the highest retrieval scores . |
| Outcome: | The proposed retrieval heads outperform other retrieval-based retrieval retrievers on BEIR benchmarks. |
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. |
| Outcome: | The proposed method outperforms conventional training with InfoNCE by a large margin on MARCO and BEIR benchmarks without using real documents. |
When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR (2025.findings-acl)
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Dayoon Ko, Jinyoung Kim, Sohyeon Kim, Jinhyuk Kim, Jaehoon Lee, Seonghak Song, Minyoung Lee, Gunhee Kim
| Challenge: | Dense retrievers encode text into embeddings to retrieve relevant documents . however, real-world corpora evolve, resulting in degraded retrieval performance . identifying when a dense retriever requires an update is critical for robust retrieval systems . |
| Approach: | They propose a task of predicting whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing. |
| Outcome: | The proposed method detects whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing. |