Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih
| Challenge: | Existing models that learn intents from labeled data are complicated and require a vast number of annotated examples to train a model. |
| Approach: | They propose a general-purpose task-aware retrieval system with instructions that can adapt to a new task without any parameter updates. |
| Outcome: | The proposed system outperforms two benchmarks on a set of domains and tasks on X2-Retrieval. |
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Weiwei Sun, Zhengliang Shi, Wu Long, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren
| Challenge: | Existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. |
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Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (2024.emnlp-main)
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| Challenge: | Experimental results show that instruction tuning improves zero-shot generalization across various tasks and improves performance of specific tasks. |
| Approach: | They propose a task selection method that leverages instruction information alone to identify relevant tasks and optimize instruction tuning for specific tasks. |
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Fine-Tuning Large Language Models with Sequential Instructions (2025.naacl-long)
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| Challenge: | Existing instruction-tuned models struggle to adhere to a query with multiple intentions, which impairs their performance when the completion of several tasks is demanded by a single command. |
| Approach: | They develop an automatic process that turns existing data into diverse and complex task chains and a new benchmark to evaluate a model’s ability to follow all the instructions in a sequence. |
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FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions (2025.naacl-long)
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Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, Luca Soldaini
| Challenge: | Modern language models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. |
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Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning (2023.acl-short)
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| Challenge: | Recent studies on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. |
| Approach: | They analyze how models utilize instructions during IT by comparing model training with altered vs. original instructions. |
| Outcome: | The proposed model outperforms naive models in low resource setting. |
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers (2025.naacl-long)
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| Challenge: | Existing information retrieval models assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings. |
| Approach: | They propose a unified instruction-aware heterogeneous knowledge retriever that builds a heterogenous retrieval space for heterogenized knowledge and follows diverse user instructions to retrieve knowledge in specified types. |
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Multi-Task Retrieval for Knowledge-Intensive Tasks (2021.acl-long)
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Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, Gargi Ghosh
| Challenge: | Knowledge-intensive tasks require large amounts of knowledge about the world . recent neural retrieval models achieve better results by learning directly from task-specific training data. |
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Robustness of Learning from Task Instructions (2023.findings-acl)
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| Challenge: | traditional supervised learning mostly works on individual tasks and requires training on a large set of task-specific examples. |
| Approach: | a new study investigates the system robustness when instructions are manipulated and paraphrased . task instructions give the model the definition of the task and allow it to output the appropriate answer . |
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Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt (2023.findings-emnlp)
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| Challenge: | Recent studies show that adding a instruction tuning stage to training large language models can improve zero-shot task generalization. |
| Approach: | They propose a method that retrieves promptspecific source prompt embeddings from training instances . they train soft prompt embeds for each prompt through prompt tuning and store the samples . |
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Disentangling Questions from Query Generation for Task-Adaptive Retrieval (2024.findings-emnlp)
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| Challenge: | Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. |
| Approach: | They propose a query generator that better adapts to wide search intents expressed in the BeIR benchmark. |
| Outcome: | The proposed query generator outperforms baselines and existing models on tasks with underexplored intents while using a query generator 47 times smaller than the previous state-of-the-art. |