Papers with Trace
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have improved the functional correctness of code translation, but execution efficiency remains overlooked. |
| Approach: | They propose a benchmark to explicitly assess execution efficiency in LLM-translated code. |
| Outcome: | The proposed benchmark identifies that execution efficiency is an essential dimension of code translation . the results highlight that correctness and efficiency are often misaligned . |
“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs (2025.findings-emnlp)
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| Challenge: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Approach: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Outcome: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
TRACE: A Corpus of Team Creative Discussions (2026.acl-long)
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| Challenge: | Existing studies on team creativity lack the ability to observe discussion dynamics from the perspective of natural language processing (NLP) Standard approaches capture participants' perceptions rather than actual behavior. |
| Approach: | They propose a corpus of 309 group discussions from 103 teams across six creative problem-solving tasks. |
| Outcome: | The proposed analysis reveals that large teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content. |