Papers by Qiancheng Zhang
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
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| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |
KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have proven highly capable in handling downstream tasks, but the token-by-token generation in autoregressive decoding results in quadratic computational complexity. |
| Approach: | They propose a method that proposes skipping certain layers to construct a draft model, which eliminates the need for additional parameters or training. |
| Outcome: | The proposed method achieves 1.31.6 speedup in LLM inference while being sensitive to domain shifts. |