Papers by Byron C Wallace
Who Taught You That? Tracing Teachers in Model Distillation (2025.findings-acl)
Copied to clipboard
| Challenge: | Xu et al., 2006, show that model distillation can imbue efficient small language models with task-specific capabilities competitive with expensive teacher LLMs. |
| Approach: | They propose to distill outputs from a large teacher model to a small student model . they propose to use part-of-speech templates as higher-order linguistic features capable of capturing distinctive signals from teacher models that persist in distilled student outputs. |
| Outcome: | The proposed model distillation technique can imbue efficient small language models with task-specific capabilities competitive with (expensive) teacher LLMs. |
Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence (2026.findings-acl)
Copied to clipboard
Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib, Ramez Kouzy, Wei Xu, Byron C Wallace, Junyi Jessy Li
| Challenge: | Existing models are overwhelmingly accurate when presented with counterfactual medical evidence . prior work explored conflicts between context and LLM parametric knowledge in the general domain . |
| Approach: | They construct a counterfactual medical QA dataset that requires models to answer clinical comparison questions with evidence from randomized controlled trials. |
| Outcome: | The proposed model overemphasizes the latter, and the model overestimates the latter. |
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
Copied to clipboard
Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li
| Challenge: | Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media . |
| Approach: | They propose that fact-checking should be approached as an interactive communication problem . they argue that social media and AI have made medical knowledge accessible . |
| Outcome: | The proposed method is based on the work of a clinical expert on social media . it reveals that the method is difficult to connect claims to clinical trials . |
Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study has shown that LLMs encode social biases and manifest in clinical tasks. |
| Approach: | They use mechanistic interpretability to uncover biases within LLMs . they find gender information is highly localized in MLP layers . |
| Outcome: | The proposed method can reveal biases and representations within LLMs in healthcare. |