Papers by Sharon Li
How Retrieved Context Shapes Internal Representations in RAG (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) is a widely adopted approach for enhancing large language models with external knowledge. |
| Approach: | They analyze how different types of retrieved documents affect the hidden states of large language models and how these internal representation shifts relate to downstream generation behavior. |
| Outcome: | The results show that context relevancy and layer-wise processing influence internal representations, providing explanations of LLMs’ output behaviors and insights for RAG system design. |
ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation (2026.acl-long)
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| Challenge: | Existing approaches to selecting a single high-quality output from multiple generations are limited in their applicability and efficiency. |
| Approach: | They propose a method that generalizes majority voting to open-ended text generation . modeX leverages structural information across multiple generation paths to select a "modal" output . |
| Outcome: | The proposed framework outperforms standard single- and multi-path baselines in open-ended tasks. |
GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models (2026.acl-long)
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| Challenge: | Existing evaluation paradigms for geographic reasoning are outcome-centric and focus on label matching, leaving the underlying linguistic reasoning chains as unexamined black boxes. |
| Approach: | They propose a dynamic, human-preference-based evaluation framework for benchmarking open-world geographic reasoning. |
| Outcome: | The proposed framework reframes evaluation as a pairwise reasoning alignment task on in-the-wild images, where human judges compare model-generated explanations based on reasoning quality, evidence synthesis, and plausibility. |
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026.acl-long)
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| Challenge: | In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. |
| Approach: | They propose a simple inference-stage enhancement method that reinforces task mapping transfer. |
| Outcome: | The proposed method strengthens task mapping transfer in multimodal models . it performs comparable to text-only ICL in zero-shot settings but degrades significantly under few-shot demonstrations. |
When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning (2026.acl-long)
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| Challenge: | Multi-agent debate (MAD) aims to improve large language model reasoning by letting multiple agents exchange answers and then aggregate their opinions. |
| Approach: | They propose a principled framework that joins sycophancy and self-bias to mitigate and quantify identity bias in multi-agent debate by removing identity markers from prompts. |
| Outcome: | The proposed framework joins identity-driven sycophancy and self-bias to mitigate and quantify identity bias in multi-agent debate. |
LAD: Learning Advantage Distribution for Reasoning (2026.findings-acl)
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| Challenge: | Recent reinforcement learning objectives focus on maximizing expected rewards, limiting diversity and exploration. |
| Approach: | They propose a distribution-matching framework that replaces advantage maximization with learning the advantage-induced distribution. |
| Outcome: | Experiments on math and code reasoning tasks show that LAD improves accuracy and diversity. |
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities (2026.acl-long)
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Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li
| Challenge: | Uncertainty quantification (UQ) for large language models is a key building block for daily applications. |
| Approach: | They propose a general formulation of agent UQ that subsumes broad classes of existing UQ setups. |
| Outcome: | The proposed framework is based on the first general formulation of agent UQ that subsumes broad classes of existing setups. |
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (2026.findings-acl)
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| Challenge: | Existing self-evaluation methods rely on a model’s ability to estimate the correctness of its own outputs, but they depend heavily on language priors and are therefore ill-suited for evaluating vision-conditioned predictions. |
| Approach: | They propose a vision-aware uncertainty quantification framework that measures how strongly a model’s output depends on visual evidence. |
| Outcome: | The proposed framework outperforms existing methods across multiple datasets. |