ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering (2026.acl-long)
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| Challenge: | Recent work in language modeling has led to effective SLMs with impressive performance levels across various benchmarks. |
| Approach: | They propose a benchmark that introduces process-level evaluation for commonsense reasoning tasks. |
| Outcome: | The proposed benchmarks show that large language models provide correct answers despite flawed reasoning processes in a substantial portion of cases. |
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| Challenge: | Existing evaluation practices are inconsistent, resulting in fragmented progress across evaluator design and benchmark development. |
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Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
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Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim
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