Papers by Litu Ou

2 papers
Context-Aware Hierarchical Merging for Long Document Summarization (2025.findings-acl)

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Challenge: Hierarchical merging is a technique used to summarize very long texts . it can amplify LLM hallucinations, increasing the risk of factual inaccuracies .
Approach: They propose to enrich hierarchical merging with context from the source document to reduce the risk of factual inaccuracies.
Outcome: The proposed methods outperform zero-shot and hierarchical merging baselines on legal and narrative datasets.
BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)

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Challenge: Existing work on confidence in LLMs is limited.
Approach: They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level.
Outcome: The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods.

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