Papers by Hongming Li

7 papers
Event-Centric Natural Language Processing (2021.acl-tutorials)

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Challenge: This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach: This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
Outcome: This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Challenge: We present a new information extraction system that can construct temporal event graphs from news documents.
Approach: They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome: The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.
NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)

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Challenge: Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations.
Approach: They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
Outcome: The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models.
Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning (2026.findings-acl)

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Challenge: Large language models are increasingly used in medicine, but expert-level clinical reasoning remains a high-complexity, high-stakes frontier.
Approach: They propose to train clinical reasoning models using a Reasoning-Oriented Data Strategy based on topological synthesis and CoT cold-start.
Outcome: The proposed pipeline outperforms existing models and outperformed the strongest open-source alternatives up to 671B in MedXpertQA.
Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)

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Challenge: Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling.
Approach: They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention.
Outcome: The proposed methods lower irregular attention entropy and narrow performance gaps.
Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models (2026.acl-long)

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Challenge: Semantic phrases (SP) are lexical combinations whose meanings or usages may not be fully derived from their individual components.
Approach: They propose to consolidate existing multiword expression resources into a unified testbed to assess language models in semantic phrase processing tasks.
Outcome: The evaluation suite covers idiomatic expressions, noun compounds, and verbal constructions.
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models (2024.emnlp-main)

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Challenge: Standard RALMs often neglect their intrinsic knowledge due to the interference from retrieved information.
Approach: They propose a new approach to improve robustness of RALMs by generating sequential reading notes for each retrieved document.
Outcome: The proposed approach outperforms standard RALMs on four open-domain QA benchmarks.

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