Papers by Fangqi Li

2 papers
VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding (2025.emnlp-main)

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Challenge: Existing benchmarks focus on text comprehension, but MLLMs lack the ability to integrate visual data over financial visuals.
Approach: They evaluate 21 state-of-the-art multimodal large language models in a zero-shot setting . they use an annotated question–answer pair from eight common financial image modalities .
Outcome: The new benchmark outperforms existing models but trailed financial experts by 14 percentage points.
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

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Challenge: Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach: They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome: The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

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