Papers by Sirui Xia
Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning. |
| Approach: | They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions. |
| Outcome: | The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions. |
FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, but struggle with tasks requiring simultaneous retrieval of multiple facts. |
| Approach: | They propose a method that refines context through successive rounds of rewriting to address this problem by finding all Crucial Texts (FACT) |
| Outcome: | The proposed method improves multi-fact retrieval performance across tasks, though improvements are less notable in general-purpose QA scenarios. |
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)
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| Challenge: | Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability. |
| Approach: | They propose a method that provides sentence-level citations in LLM-generated responses. |
| Outcome: | The proposed method achieves 90% accuracy in long-form question-answering tasks. |