Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering
Authors: Haowei Du, Huishuai Zhang, Dongyan Zhao


Reviewer paper summaries: The paper tackles the generative question generation task on the challenge of leveraging noisy evidence. They enhance LLM by introducing auxiliary instruction tuning tasks. The tasks are designed around <Question, Answer, Evidence> triplets, including predicting Q given <E, A>, predicting A given <E, Q>,  and predicting Evidence given <Q, A>. With parameter efficient fine-tuning, the model achieves SOTA performance on MultiRC and QASPER datasets.
Summary of strengths: 1. Clear description of the proposed method.
2. Experimental results show the effectiveness of the designed auxiliary tasks, and show the relevance of the auxiliary tasks and target tasks.
Summary of weaknesses: 1. Maybe I missed some details, but I didn't see the CAD and RHO paper report fine-tuning results on MultiRC and Qasper. 
2. As far as I know, CAD doesn't require additional fine-tuning. RHO doesn't use Llama, if the reference is correct. I don't think they are  comparable with the proposed method.
Comments, suggestions and typos: 1. See above. If the authors could address my concern by pointing out which section/line in those baseline papers show the results and settings, I would modify the review accordingly. Appreciate it! 
2. Is there a basic baseline of adapter fine-tuning with Llama? I'm not sure whether the Llama result in Table 1 is the with full fine-tuning one adapter. By the way, I couldn't find result MultiRC and Qasper in Llama2 technical report, neither.
3. Table 1, one name of proposed method is "EAT-QA", while another is "EATQA".
4. Figure 3 & 4, a larger font would be more paper reader friendly.
Overall assessment scores: 2