Papers by Jia Bi
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)
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Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai
| Challenge: | Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing. |
| Approach: | They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards. |
| Outcome: | The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations . |
Token-Wise Kernels (TWiKers) for Vicinity-Aware Attention in Transformers (2026.findings-eacl)
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| Challenge: | Token-Wise Kernels (TWiKers) are a novel enhancement to transformers that learn token-specific convolutional kernels applied to the keys or values. |
| Approach: | They propose a transformer enhancement that learns token-specific convolutional kernels applied to the keys or values. |
| Outcome: | The proposed transformers learn token-specific convolutional kernels applied to the keys or values . the results show that content words retain self-focus while function words shift attention toward their neighbors . |