Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation (2025.naacl-long)
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| Challenge: | Existing methods to improve language models' performance do not exploit this phenomenon . |
| Approach: | They propose to use contextual information to select and construct prompts that improve model performance. |
| Outcome: | The proposed methods show that the mutual information between a context and a question is an effective gauge for language model performance. |
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| Challenge: | Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. |
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From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
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Anwesan Pal, Karen Hovsepian, Tinghao Guo, Mengnan Zhao, Somendra Tripathi, Nikos Kanakaris, George Mihaila, Sumit Nigam
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Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
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| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
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| Challenge: | Existing approaches to retrieval augmented generation (RAG) are based on parametric knowledge and external knowledge. |
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| Challenge: | Existing methods for augmented large language models suffer from irrelevant retrieved content . existing methods struggle to adapt compression rates for different context, maintain low latency . |
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| 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. |
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Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models (2024.naacl-long)
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| Challenge: | Large language models (LMs) excel in retrieving popular facts, but encounter difficulty with infrequent entity-relation pairs compared to retrievers. |
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LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation (2025.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) exhibit significant biases in evaluation tasks, especially in preferentially rating and favoring self-generated content. |
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