Papers with fine-grained evaluation of

3 papers
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

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Challenge: Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.
Approach: They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities.
Outcome: The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.
Content Differences in Syntactic and Semantic Representation (N19-1)

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Challenge: Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
Approach: They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes.
Outcome: The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement.
Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques (2020.lrec-1)

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Challenge: a recent study shows that human translators often resort to different non-literal translation techniques besides literal translation . however, they receive less attention in developing natural language processing (NLP) applications.
Approach: They propose to have a better semantic control of extracting paraphrases from bilingual parallel corpora.
Outcome: The proposed method can automatically recognize different non-literal translation techniques . the results confirm the hypothesis of the proposed method .

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