COFFEQA introduces a counterfactual-guided few‑shot distillation method for open‑book question answering, leveraging minimally modified contexts that prompt the teacher model to produce alternative answers. By pairing factual samples with validated counterfactuals—filtered using a new Context Following Difficulty metric—COFFEQA constrains the student’s decision boundary to better match the teacher’s. Experiments on multiple QA benchmarks show that COFFEQA consistently outperforms factual‑only distillation, achieving significant gains in EM and F1 scores with a limited few‑shot budget.
By Kavinda Kehelella, Sanghamitra Dutta
The paper introduces a joint fact‑verification score that evaluates both answers and the evidence submitted with them. On the FEVEROUS dataset, replacing the DCUF evidence with UnifEE evidence improves the strict score by about 9.6 percentage points, while answer accuracy rises only 1.96 points. The study also shows that increasing context length for large language models yields modest evidence‑gain improvements, and that detailed answer‑evidence analyses uncover patterns missed by aggregate metrics.
By Han Chen, Yingrui Li
The paper introduces ReDiST, a framework that constructs student‑conditioned reasoning traces to enhance learning beyond answer‑only supervision. ReDiST revises student attempts using evidence and a verified answer, then selects the best traces with a frozen reader that evaluates answer recovery and probability. The distilled student is trained offline, and experiments on QA benchmarks show improved performance over baseline trace methods and highlight varying effectiveness across source and transfer settings.
By Shaoang Li, Yanhang Shi, Yufei Li, Mingfu Liang, Frank Shyu, Sandeep Pandey, Luke Simon, Xi Liu, Jian Li
The paper introduces Evidence Sufficiency Boundary Training, a framework that teaches models to abstain from answering until the supplied evidence is fully sufficient, and to remain stable when additional redundant evidence is added. By constructing ordered evidence chains from datasets such as HotpotQA, 2WikiMultiHopQA, and MuSiQue, the method applies level supervision, a boundary flip margin, post‑boundary stability, and answer recall protection. Experiments with Qwen2.5‑3B‑Instruct and LoRA adaptation show improved boundary localization (flip accuracy 0.807 vs 0.781) and a lower unsupported‑answer rate (0.095 vs 0.101) while maintaining competitive raw QA F1.
By Haruto Sato, Yuki Tanaka, Ren Nakamura, Aoi Kobayashi, Mei Ito
arXiv:2609.37491v1 Announce Type: cross
Abstract: Retrieval-augmented language models are expected to answer from the retrieved evidence, but in practice they often keep answering when that evidence...
By Zeyan Li, Qirong Guo, SIyuan Qiu, Hu Xu, Chun Li, Jianfeng Xu
Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult because partial evidence can make an unsupported a...