Regime Boundary Alignment for Evidence-Gated Question Answering
Retrieval-augmented language models are expected to answer from the retrieved evidence, but in practice they often keep answering when that evidence is missing. We trace this behavior to the training signal: answer-focused fine-tuning assigns no target to unsupported contexts, so it cannot distinguish a reader that abstains from one that guesses, and unsupported answering stays near 100% even as supported accuracy improves. We introduce Regime Boundary Alignment (RBA), which trains a single reader on matched variants of the same question and gold answer. The reader is trained to produce the gold answer when the context supports it, including when conflicting evidence is also present, and ...
| Authors | Zeyan Li, Qirong Guo, SIyuan Qiu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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